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Journal articles on the topic 'Image processing Remote sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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1

Camps-Valls, Gustavo, Devis Tuia, Luis Gómez-Chova, Sandra Jiménez, and Jesús Malo. "Remote Sensing Image Processing." Synthesis Lectures on Image, Video, and Multimedia Processing 5, no. 1 (2011): 1–192. http://dx.doi.org/10.2200/s00392ed1v01y201107ivm012.

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2

Wang, Yi Ding, and Shuai Qin. "Remote Sensing Image Mosaic Algorithm." Key Engineering Materials 500 (January 2012): 716–21. http://dx.doi.org/10.4028/www.scientific.net/kem.500.716.

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In the field of remote sensing, the acquirement of higher resolution of remote sensing images has become a hot spot issue with widely use of high resolution of remote sensing images. This paper focus on the characteristics of high resolution remote sensing images, on the basis of fully considerate of the correlation between geometric features and image pixels, bring forward a fusion of image mosaic processing algorithm. With this algorithm, the surface features can be well preserved after the processing of mosaic the remote sensing images, and the overlapping area can transit naturally, it will be better for the post-processing, analysis and application.
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3

Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, et al. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13 (2021): 2432. http://dx.doi.org/10.3390/rs13132432.

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Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.
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Jiang, W., S. Chen, X. Wang, Q. Huang, H. Shi, and Y. Man. "REMOTE SENSING IMAGE QUALITY ASSESSMENT EXPERIMENT WITH POST-PROCESSING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 665–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-665-2018.

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This paper briefly describes the post-processing influence assessment experiment, the experiment includes three steps: the physical simulation, image processing, and image quality assessment. The physical simulation models sampled imaging system in laboratory, the imaging system parameters are tested, the digital image serving as image processing input are produced by this imaging system with the same imaging system parameters. The gathered optical sampled images with the tested imaging parameters are processed by 3 digital image processes, including calibration pre-processing, lossy compression with different compression ratio and image post-processing with different core. Image quality assessment method used is just noticeable difference (JND) subject assessment based on ISO20462, through subject assessment of the gathered and processing images, the influence of different imaging parameters and post-processing to image quality can be found. The six JND subject assessment experimental data can be validated each other. Main conclusions include: image post-processing can improve image quality; image post-processing can improve image quality even with lossy compression, image quality with higher compression ratio improves less than lower ratio; with our image post-processing method, image quality is better, when camera MTF being within a small range.
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Zhao, Shang Min, Wei Ming Cheng, and Xi Chen. "Exploration of Remote Sensing Image Processing Method for Glacial Geomorphology Research." Key Engineering Materials 500 (January 2012): 437–43. http://dx.doi.org/10.4028/www.scientific.net/kem.500.437.

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Taking Mt. Namjagbarwa region as an example, this paper explores a complete remote sensing image processing method for glacial geomorphology research. Based on the selection of Landsat7 ETM+ images, the remote sensing image processing method such as band selection, overlap, fusion, mosaic and so on is carried out. The result shows: ① right selection of remote sensing images and proper process based on the characteristics of research area and research purpose, not only reduce the process difficulty, but make a firm foundation for subsequent glacial geomorphology research; ②according to the computation of correlation coefficient between fusion images and original multi-spectral images, panchromatic high resolution images, the result shows that the principle component transformation method has better effect than IHS transformation method in remote sensing image fusion process.
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Li, J., J. Sheng, Y. Chen, et al. "A WEB-BASED LEARNING ENVIRONMENT OF REMOTE SENSING EXPERIMENTAL CLASS WITH PYTHON." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2020 (August 24, 2020): 57–61. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2020-57-2020.

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Abstract. Remote sensing course is a general disciplinary required course of human geography and urban-rural planning major. Its class hour is 48, including theoretical classes and experimental classes. Rapid technological developments is remote sensing area demand quick and steady changes in the education programme and its realization, especially in experimental classes. Experimental classes include: introduction to remote sensing software and basic operations, remote sensing data pre-processing (input, output, 2D and 3D terrain display, image cut, image mosaic, and projection transformation), remote sensing image enhancement, remote sensing image transformation, computer aided classification, image interpretation, and remote sensing image terrain analysis. There are two difficulties in the remote sensing experimental classes. First, it cost a lot of time to prepare the remote sensing software and the remote sensing images. Second, some students just want to use the remote sensing as a tool to investigate environment changing, some other students may want to study more remote sensing image processing technologies. A web-based learning environment of remote sensing is developed to facilitate the application of remote sensing experimental teaching. To make the learning more effective, there are eight modules including four optional modules. The Python programming language is chosen to implement the web-based remote sensing learning environment. The web-based learning environment is implemented in a local network server, including the remote sensing data processing algorithms and many satellite image data. Students can easily exercise the remote sensing experimental courses by connecting to the local network server. It is developed mainly for remote sensing experimental course, and also can be adopted by digital image processing or other courses. The feature of web-based learning may be very useful as the online education adopted because of Corona Virus Disease 2019. The results are encouraging and some recommendations will be extracted for the future.
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Fu, N., L. Sun, H. Z. Yang, J. Ma, and B. Q. Liao. "RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 565–68. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-565-2020.

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Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.
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8

Li, Su, Wen Chao Wang, Lu Wen Li, and Jian Jun Zhou. "Survey of Support Vector Machine in the Processing of Remote Sensing Image." Advanced Materials Research 774-776 (September 2013): 1567–72. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1567.

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Support vector machine is a kind of machine learning algorithm which is based on statistical learning theory and VC dimension theory and structural risk minimization principle, it can solve data classification and regression problems. With the in-depth research of support vector machine, in the field of remote sensing image processing applications are also obtained the very big development. This paper first gives a brief introduction of the theory of support vector machine, and then summarized the progress in remote sensing image compression, geometric correction of remote sensing images, processing of remote sensing image data classification research, finally proposed the trend of the support vector machine in application and development in the field of remote sensing image processing problems.
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9

Shu, Chang, and Lihui Sun. "Automatic target recognition method for multitemporal remote sensing image." Open Physics 18, no. 1 (2020): 170–81. http://dx.doi.org/10.1515/phys-2020-0015.

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AbstractThe traditional target recognition method for the remote sensing image is difficult to accurately identify the specified targets from the massive remote sensing image data. Based on the theory of multitemporal recognition, an automatic target recognition method for the remote sensing image is proposed in this article. The proposed recognition method includes four modules: automatic segmentation of multitemporal remote sensing image, automatic target extraction of multitemporal remote sensing image, automatic processing of multitemporal remote sensing image, and automatic recognition of multitemporal remote sensing image. The automatic segmentation of the image target is introduced. The effectiveness of the segmentation technology is verified through the kernel function bandwidth algorithm. Linear feature extraction is used to extract the segmented image. The image extraction processing is described, which includes image profile analysis, image preprocessing, image feature analysis, the region of interest localization, image enhancement processing, recognition processing, and result output. According to the theory of pattern recognition, three different feature recognition images are given, which are partial separable recognition, weakly separable recognition, and fully separable recognition, and then, a new image recognition method is designed. To verify the practical application effect of the recognition method, the proposed method is compared with the traditional recognition method. Experimental results show that the proposed method can accurately identify the specified objects from the massive remote sensing image data and has a high potential for development. This article has an important guiding significance for image recognition.
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10

Fan, Peili. "Combined with Local Neighborhood Characteristics and Remote Sensing Image Fusion Method of C-BEMD." International Journal of Circuits, Systems and Signal Processing 15 (August 12, 2021): 936–44. http://dx.doi.org/10.46300/9106.2021.15.100.

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For the sake of ameliorate the high resolution recognition capacity building remote sensing images, a remote sensing image fusion method based on local neighborhood characteristics and C-BEMD is advanced. The building remote sensing image acquisition model and the building remote sensing image picture element edge feature detection model are designed. The wavelet multi-scale denoising method is used to suppress the fuzzy spread of picture element feature points between image residual units, extract the geometric feature points of image sequence, and process the building remote sensing image block by block. The global residual learning and message fusion of building remote sensing image are implemented. The local neighborhood feature matching method is used to reconstruct the building remote sensing image region. Combined with the C-BEMD empirical mode decomposition method, the building remote sensing image fusion and feature point matching in affine region are implemented, and the block image template matching method is used to realize the automatic fusion and recognition of building remote sensing image. Simulation results show that this method has high precision in constructing remote sensing image fusion and good positioning performance in constructing remote sensing image feature points.
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11

Jin, Liang, and Guodong Liu. "An Approach on Image Processing of Deep Learning Based on Improved SSD." Symmetry 13, no. 3 (2021): 495. http://dx.doi.org/10.3390/sym13030495.

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Compared with ordinary images, each of the remote sensing images contains many kinds of objects with large scale changes, providing more details. As a typical object of remote sensing image, ship detection has been playing an essential role in the field of remote sensing. With the rapid development of deep learning, remote sensing image detection method based on convolutional neural network (CNN) has occupied a key position. In remote sensing images, the objects of which small scale objects account for a large proportion are closely arranged. In addition, the convolution layer in CNN lacks ample context information, leading to low detection accuracy for remote sensing image detection. To improve detection accuracy and keep the speed of real-time detection, this paper proposed an efficient object detection algorithm for ship detection of remote sensing image based on improved SSD. Firstly, we add a feature fusion module to shallow feature layers to refine feature extraction ability of small object. Then, we add Squeeze-and-Excitation Network (SE) module to each feature layers, introducing attention mechanism to network. The experimental results based on Synthetic Aperture Radar ship detection dataset (SSDD) show that the mAP reaches 94.41%, and the average detection speed is 31FPS. Compared with SSD and other representative object detection algorithms, this improved algorithm has a better performance in detection accuracy and can realize real-time detection.
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12

UEKI, TOSHIAKI. "Example of image processing. Processing of image received by remote sensing." Journal of the Japan society of photogrammetry and remote sensing 24 (1985): 68–70. http://dx.doi.org/10.4287/jsprs.24.special1_68.

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13

Sakurai-Amano, Takako, and Joji Iisaka. "PC based image processing for remote sensing." Journal of the Visualization Society of Japan 18, Supplement1 (1998): 85–88. http://dx.doi.org/10.3154/jvs.18.supplement1_85.

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14

Zhong, Yanfei, Zexuan Zhu, and Yew Soon Ong. "Soft computing in remote sensing image processing." Soft Computing 20, no. 12 (2016): 4629–30. http://dx.doi.org/10.1007/s00500-016-2368-7.

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15

Wang, Yu Jing, Cai Hong Yuan, and Lin Wu. "Design of Remote Sensing Image Files Unified Processing Component." Advanced Materials Research 926-930 (May 2014): 3026–29. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3026.

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This paper presents a component of unified processing for remote sensing image files based on GDAL. The component, which developed an interface specification for unified remote sensing image processing, encapsulates processing operations of remote sensing image files to form an integral, such as GeoTIFF, HDF, SHP, etc.. The component effectively achieves a variety format image files to read and write, save, display, and conversion, to facilitate the development and application of remote sensing image processing system, and reduce the cost of software development.
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16

PERNER, PETRA, ALEC HOLT, and MICHAEL RICHTER. "Image processing in case-based reasoning." Knowledge Engineering Review 20, no. 3 (2005): 311–14. http://dx.doi.org/10.1017/s0269888906000671.

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This commentary summarizes case-based reasoning (CBR) research applied to image processing. It includes references to the systems, workshops, and landmark publications. Image processing includes a variety of image formats, from computer tomography images to remote sensing and spatial data sets.
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17

Wang, Xiao Yu, Guo Qing Li, Wen Yang Yu, and Quan Zou. "Research on Method for Massive Pixel-Level Remote Sensing Image Processing Based on Hadoop." Applied Mechanics and Materials 333-335 (July 2013): 1224–30. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1224.

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Recently, global change research has reflected the great challenge of massive distributed remote sensing image processing. Faced with such challenge, massive pixel-level remote sensing image processing reconstruction based on Hadoop is proposed, which focuses on the support of data format and the design of paralle computing. In order to support a variety of formats of remote sensing images and simplify the process of data parse, the processing flow transforms the remote sensing image into image information in binary format, as well as metadata information in xml format. Compared with converting to text format, there are two advantages for this conversion, reducing the amount of data after converted and remaining metadata information. To avoid MapReduce parallel computing performance interference caused by the algorithmic complexity, remote sensing image point operation is selected to do research about the design of parallel computing. The experimental results show that the proposed method has good scalability in the distributed Hadoop environment, along with the changing of the data quantity.
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18

Xiu, Hongling, and Fengyun Yang. "Batch Processing of Remote Sensing Image Mosaic based on Python." International Journal of Online Engineering (iJOE) 14, no. 09 (2018): 208. http://dx.doi.org/10.3991/ijoe.v14i09.9226.

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In the process of remote sensing image processing, analysis and interpretation, it is usually necessary to combine several local images into a complete image. Aiming at the shortcoming of long and complicated process of conventional semi-automatic video stitching. In this paper, using the splicing method of pixels, based on the Python interface of ArcGIS 10.1 platform, the idea of programming language is introduced and batch mosaic of remote sensing images is realized. Through the comparison with the image processing software, it is found that this method can shorten the time of image mosaic and improve the efficiency of splicing, which is convenient for later image analysis and other work under the premise of ensuring the accuracy.
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Tutatchikov, Valeriy, and Mikhail Noskov. "Application of high frequency filtration to remove cloudiness in Earth remote sensing images." E3S Web of Conferences 223 (2020): 02010. http://dx.doi.org/10.1051/e3sconf/202022302010.

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At present, methods of digital processing of Earth remote sensing images are widely used to improve the image quality. For example, many images are discarded due to high clouds in the images, which obscure objects of interest. In this paper, the possibility of using high- frequency global filters to reduce cloudiness in the image is considered, and the results of image enhancement are shown.
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Li, Chu Yan, Xian Wei Shi, Xiao Jing Li, Hai Yu Li, and Lin Deng. "Analysis of the Computer Processing System of Remote Sensing Satellite Images." Advanced Materials Research 756-759 (September 2013): 3987–91. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3987.

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Remote sensing satellite images can intuitively reflect the information of the Earth's surface. The computer image processing system is of the advantages of high-precision and low-cost. It has a strong application value to study the computer processing system of remote sensing satellite image. The paper first discussed the design principles of the computer processing system and the implementation of its workflow, and then the application of the image processing system is briefly analyzed.
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Pohl, C., and Y. Zeng. "Development of a fusion approach selection tool." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 139–44. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-139-2015.

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During the last decades number and quality of available remote sensing satellite sensors for Earth observation has grown significantly. The amount of available multi-sensor images along with their increased spatial and spectral resolution provides new challenges to Earth scientists. With a Fusion Approach Selection Tool (FAST) the remote sensing community would obtain access to an optimized and improved image processing technology. Remote sensing image fusion is a mean to produce images containing information that is not inherent in the single image alone. In the meantime the user has access to sophisticated commercialized image fusion techniques plus the option to tune the parameters of each individual technique to match the anticipated application. This leaves the operator with an uncountable number of options to combine remote sensing images, not talking about the selection of the appropriate images, resolution and bands. Image fusion can be a machine and time-consuming endeavour. In addition it requires knowledge about remote sensing, image fusion, digital image processing and the application. FAST shall provide the user with a quick overview of processing flows to choose from to reach the target. FAST will ask for available images, application parameters and desired information to process this input to come out with a workflow to quickly obtain the best results. It will optimize data and image fusion techniques. It provides an overview on the possible results from which the user can choose the best. FAST will enable even inexperienced users to use advanced processing methods to maximize the benefit of multi-sensor image exploitation.
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Zhang, Jing, Qianlan Zhou, Li Zhuo, Wenhao Geng, and Suyu Wang. "A CBIR System for Hyperspectral Remote Sensing Images Using Endmember Extraction." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (2017): 1752001. http://dx.doi.org/10.1142/s0218001417520012.

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With the rapid development of remote sensing technology, searching the similar image is a challenge for hyperspectral remote sensing image processing. Meanwhile, the dramatic growth in the amount of hyperspectral remote sensing data has stimulated considerable research on content-based image retrieval (CBIR) in the field of remote sensing technology. Although many CBIR systems have been developed, few studies focused on the hyperspectral remote sensing images. A CBIR system for hyperspectral remote sensing image using endmember extraction is proposed in this paper. The main contributions of our method are that: (1) the endmembers as the spectral features are extracted from hyperspectral remote sensing image by improved automatic pixel purity index (APPI) algorithm; (2) the spectral information divergence and spectral angle match (SID–SAM) mixed measure method is utilized as a similarity measurement between hyperspectral remote sensing images. At last, the images are ranked with descending and the top-[Formula: see text] retrieved images are returned. The experimental results on NASA datasets show that our system can yield a superior performance.
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Yuan, Shuai, Guo Yun Zhang, Jian Hui Wu, and Long Yuan Guo. "Study of Remote Sensing Image Registration System." Applied Mechanics and Materials 530-531 (February 2014): 573–76. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.573.

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Remote sensing image registration system has been widely applied for agriculture, forestry, geology, ocean, meteorology, hydrology and military field, which studies image correction, enhancement and registration based on digital image processing technology. This paper presents system design and realization of registration technology for remote sensing image. Remote sensing image has geometric distortion and needs digital correction by adopting polynomial method at first. Then five kinds of technology are offered to enhance the contrast of remote sensing image including histogram enhancement, grey level transformation enhancement, image smoothing, image sharpening and pseudo color enhancement. Image geometric registration algorithm is carried out to fuse images of the same area acquired at different times on different wave band at last. Test result shows that the method presented has good accuracy and quick speed for realtime application.
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Sun, L., and X. S. Gan. "ANALYSIS OF DENOISING INTERPRETATION OF REMOTE SENSING IMAGE BASED ON ICA-WAVELET TRANSFORM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 411–14. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-411-2020.

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Abstract. The noise will blur the key information of the remote sensing image, such as edge texture and important feature information, which will result in the loss of key information contained in the remote sensing image, resulting in the degradation of the overall quality of the image, which will bring difficulties to the interpretation work. Therefore, in order to obtain higher precision, signal-to-noise ratio and improve the quality of remote sensing image, denoising the remote sensing image containing noise is a crucial step and processing step for image remote sensing image application.In this paper, the ICA wavelet analysis algorithm is applied to the application of real-time remote sensing image denoising. A series of pre-processing procedures such as control point correction, image fusion and image mosaic are carried out on the Asian sub-level remote sensing image, and the signal-to-noise ratio of the remote sensing image is adopted. (SNR/dB) and mean square error (RMSE) verify the image quality after denoising.
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Zhou, Xiao Hu. "Study on Remote Sensing Faults Information Extraction in Eastern Kunlun, NW China." Applied Mechanics and Materials 380-384 (August 2013): 3958–61. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3958.

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Choosing the junction of Altun-Kunlun orogenic belt as the anatomical area of extracting complex texture and structure information from remote sensing images, make full use of multi-band remote sensing images to reflect the characteristics of the properties, to extract hidden information through image processing. Analyzing the structure elements by geological body, rock combination, linear and banded structure, penetrative and non-penetrative planar structure, folds, to carry out the surficial composition and structure research of the the junction of Altun-Kunlun orogenic belt, identifying different geological bodies, the fault zones, ductile shear zones, superimposed folds and different strain zones, the different types of foliation, clarifying the characteristics of multi-source remote sensing image from the angle of the image processing methods, proposing new remote sensing image extraction methods and recognition of structural information technology and new understanding of the regional geology.
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Tripathi, Rakesh, and Neelesh Gupta. "A Review on Segmentation Techniques in Large-Scale Remote Sensing Images." SMART MOVES JOURNAL IJOSCIENCE 4, no. 4 (2018): 7. http://dx.doi.org/10.24113/ijoscience.v4i4.143.

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Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Segmentation is considered as one of the main steps in image processing. It divides a digital image into multiple regions in order to analyze them. It is also used to distinguish different objects in the image. Several image segmentation techniques have been developed by the researchers in order to make images smooth and easy to evaluate. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper a review of basic image segmentation techniques of satellite images is presented.
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Li, Hongchao, and Fang Wu. "Conversion and Visualization of Remote Sensing Image Data in CAD." Computer-Aided Design and Applications 18, S3 (2020): 82–94. http://dx.doi.org/10.14733/cadaps.2021.s3.82-94.

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In this paper, a process visualization model for remote sensing image classification algorithms is constructed to analyze the current processing characteristics of process visualization in remote sensing application systems. The usability of the model is verified in a remote sensing application system with a remote sensing image classification algorithm based on support vector machines as an example. Given the characteristics of remote sensing applications that require high visualization process and a large amount of data processing, the basic process of an image classification algorithm for remote sensing applications is summarized by analyzing the basic process of existing image classification algorithms in remote sensing applications, taking into account the characteristics of process visualization. Based on the existing process of remote sensing image classification algorithm, a process visualization model is proposed. The model takes a goal-based process acts as the basic elements of the model, provides visualization functions and interfaces for human-computer interaction through a human-computer interaction selector, and uses a template knowledge base to save processing data and realize the description of customized processes. The model has little impact on the efficiency and accuracy of the support vector machine-based remote sensing image classification algorithm during the process of process visualization and customization. Finally, the application of the model to integrate business processing of earth observation can address the problem of process customization visualization for remote sensing applications to some extent.
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Reji, A. Amala Arul, and S. Muruganantham. "Building Detection From Satellite Images for Urban Planning Using MATLAB-Based Pattern Matching Method." International Journal of Creative Interfaces and Computer Graphics 10, no. 2 (2019): 17–28. http://dx.doi.org/10.4018/ijcicg.2019070102.

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Remote sensing and satellite image processing has many applications and an emerging field of trending in recent research. Remote sensing methods for extraction of information are used to obtain information related to the Earth's resources and environment for most government-based research applications. Few enhanced hybrid algorithms are reported and in use for real-world applications in recent years by extensive research for digital image processing. The digital images and satellite images are now extensively used for its information, which have many advantages over present analog image processing methods. The satellite data are larger and carry enormous amounts of information for applications like land description, crop assessment, cultivation monitoring, meteorology, regional description, underwater analysis, sea water interpretation, and so on. This paper analyses the requirements of better interpretation methods of received/captured images and newer processing techniques required. This paper presents complete study and pattern matching research in image processing tools using MATLAB programming and its capabilities that could be used in remote sensing applications in the future.
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29

Zhang, Can. "Remote Sensing Image Processing Using Wavelet Fractal Interpolation." Journal of Computer Research and Development 42, no. 2 (2005): 247. http://dx.doi.org/10.1360/crad20050210.

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30

Ablameyko, S., and B. Beregov. "Remote sensing image processing in geographic information systems." Computing & Control Engineering Journal 7, no. 5 (1996): 235–39. http://dx.doi.org/10.1049/cce:19960508.

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31

Jensen, John R., and Kalmesh Lulla. "Introductory digital image processing: A remote sensing perspective." Geocarto International 2, no. 1 (1987): 65. http://dx.doi.org/10.1080/10106048709354084.

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32

Tailor, Anita. "Introductory digital image processing: a remote sensing perspective." Image and Vision Computing 4, no. 4 (1986): 229. http://dx.doi.org/10.1016/0262-8856(86)90052-1.

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Zhong, Yanfei, Ailong Ma, Yew soon Ong, Zexuan Zhu, and Liangpei Zhang. "Computational intelligence in optical remote sensing image processing." Applied Soft Computing 64 (March 2018): 75–93. http://dx.doi.org/10.1016/j.asoc.2017.11.045.

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34

Weng, Qihao. "Remote Sensing Time Series Image Processing. First Edition." Photogrammetric Engineering & Remote Sensing 87, no. 8 (2021): 545–46. http://dx.doi.org/10.14358/pers.87.8.545.

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35

Wang, C., F. Hu, X. Hu, S. Zhao, W. Wen, and C. Yang. "A HADOOP-BASED DISTRIBUTED FRAMEWORK FOR EFFICIENT MANAGING AND PROCESSING BIG REMOTE SENSING IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (July 10, 2015): 63–66. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-63-2015.

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Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.
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Wang, Jia Feng, Xi Min Cui, De Bao Yuan, Jing Jing Jin, Ya Hui Qiu, and Huan Liu. "Segmenting Algorithm and Publishing Based on UVA Image." Applied Mechanics and Materials 195-196 (August 2012): 594–98. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.594.

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With the continuous development of UAV remote sensing technology, UAV remote sensing will become one of the main airborne remote sensing platforms, Image acquisition and its post-processing of the UAV remote sensing have become focus of todays study. This paper presents an idea of image segmentation and image publication of UAV remote sensing, and provides reliable information for decision makers. It possesses a certain value.
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Wei, Chao, Dong Mei Liu, Fan Wang, and Ling Yan Chen. "Fusion Research of Remote Sensing Image Based on Compressive Sensing." Applied Mechanics and Materials 380-384 (August 2013): 3637–42. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3637.

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Compressive Sensing provides a new method of signal processing, when the image signal is sparse or can be com-pressed, it is possible to substantially lower than the Nyquist sampling rate, the sampling mode of the image signal is sampled, and by recovery algorithms to restore the image signal. This theory can greatly reduce the amount of data calculated in the storage, processing and transmission of the image signal. Based on this theory, the paper presents the method of remote sensing image fusion in compressed sensing domain. Firstly, the image for fast Fourier transform and measurement sampling, namely to obtain the compressed perception domain data, and then using the weighted data fusion, the final fused image is obtained by solving the optimization problem of the reconstructed image. Through the experimental proved that, this fusion method deal less data but fusion effect good.
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Zhang, Jie, Minquan Feng, and Yu Wang. "Automatic Segmentation of Remote Sensing Images on Water Bodies Based on Image Enhancement." Traitement du Signal 37, no. 6 (2020): 1037–43. http://dx.doi.org/10.18280/ts.370616.

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By virtue of high-resolution remote sensing satellites, there is a possibility to analyze remote sensing images on water bodies through digital image processing (DIP). In many remote sensing images, however, the water bodies have similar gray values as other ground objects. To effectively distinguish water bodies from other ground objects in these images, this paper proposes a logarithmic enhancement method for remote sensing images on water bodies based on adaptive morphology. The proposed method can filter the noise of non-target area, and enhance the water body in the original image. On this basis, a morphology-based segmentation method was designed for remote sensing images on water bodies. Experimental results show that our method achieved a high segmentation accuracy, controlling the mean segmentation error at below 1.32%.
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Geng, X., Q. Xu, S. Xing, Y. F. Hou, C. Z. Lan, and J. J. Zhang. "PHOTOGRAMMETRIC PROCESSING OF PLANETARY LINEAR PUSHBROOM IMAGES BASED ON APPROXIMATE ORTHOPHOTOS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 391–96. http://dx.doi.org/10.5194/isprs-archives-xlii-3-391-2018.

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It is still a great challenging task to efficiently produce planetary mapping products from orbital remote sensing images. There are many disadvantages in photogrammetric processing of planetary stereo images, such as lacking ground control information and informative features. Among which, image matching is the most difficult job in planetary photogrammetry. This paper designs a photogrammetric processing framework for planetary remote sensing images based on approximate orthophotos. Both tie points extraction for bundle adjustment and dense image matching for generating digital terrain model (DTM) are performed on approximate orthophotos. Since most of planetary remote sensing images are acquired by linear scanner cameras, we mainly deal with linear pushbroom images. In order to improve the computational efficiency of orthophotos generation and coordinates transformation, a fast back-projection algorithm of linear pushbroom images is introduced. Moreover, an iteratively refined DTM and orthophotos scheme was adopted in the DTM generation process, which is helpful to reduce search space of image matching and improve matching accuracy of conjugate points. With the advantages of approximate orthophotos, the matching results of planetary remote sensing images can be greatly improved. We tested the proposed approach with Mars Express (MEX) High Resolution Stereo Camera (HRSC) and Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) images. The preliminary experimental results demonstrate the feasibility of the proposed approach.
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Shen, Xiao Le, Zhen Feng Shao, Hui Luo, and Wei Cheng. "An Object-Oriented Shadow Detection Approach of Remote Sensing Image." Key Engineering Materials 474-476 (April 2011): 1038–43. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1038.

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Shadows widely exist in high-resolution remote sensing images and affect image interpretation in certain degree. Improving the accuracy and efficiency of shadow region detection is always a significant problem in remote sensing image processing field. In this paper, an object-oriented shadow detection approach of remote sensing image is proposed on the basis of analyzing the characteristics of the shadow object. Experimental results indicate the efficiency and validity of our object-oriented approach for shadow detection compared with conventional pixel-level methods.
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41

He, Qing Song, and Fan Gui Zeng. "Survey and Evaluation of Coalfield Geological Exploration and Coal Resources Based on Remote Sensing Technology." Applied Mechanics and Materials 380-384 (August 2013): 3930–33. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3930.

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With the development of computer science and information technology, computer data mining technology is updated, which makes the image mosaic technology that can detail data processing for remote sensing image. On this basis, this paper uses advanced computer image processing technology to carry on the combination of wavelet decomposition, combined with the GIS remote sensing technology, the coal exploration and investigation evaluation technique are carried out in-depth discussion. The paper establishes the mathematical model of GIS remote sensing image processing, and through the wavelet decomposition method, the function of image processing is given. In the third part, combined with the MATLAB data processing software, coal GIS satellite remote sensing image is carried out resource evaluation by the programming operation and the size distribution of coal rock is drawn, the coal content evaluation data table of remote sensing area is finally obtained, in which the content of No. 1 coal seam is highest reached 9860 tons, the horizontal extension of four coal seams is between 200-500m, and the longitudinally is extending between 50-100m.
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Guo, Hong Tao, Zhi Guo Chang, and Shan Wei He. "Geostationary Meteorological Satellite Data Processing System." Advanced Materials Research 181-182 (January 2011): 257–60. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.257.

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In order to design a set of geostationary meteorological satellite data processing system，which have common data processing，practical remote sensing products and rich visual stylet，used VC++6.0 MFC and dynamic link library and the mature remote sensing products processing algorithms, to design it. Multi-satellite source geostationary meteorological satellite data are integrated, remote sensing products are generated, for example, cloud detection, cloud classification, cloud top height. The original cloud image, remote sensing and geographic information products are displayed vividly. The three-dimensional cloud image processing retrieval based on cloud detection product takes into account the visual effect, and also has a clear physical meaning, practicality is strong. The system makes geostationary meteorological satellite information play a more important role in the current weather forecast.
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43

Xu, R. G., G. Qiao, Y. J. Wu, and Y. J. Cao. "EXTRACTION OF RIVERS AND LAKES ON TIBETAN PLATEAU BASED ON GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1797–801. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1797-2019.

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<p><strong>Abstract.</strong> Tibetan Plateau (TP) is the most abundant area of water resources and water energy resources in China. It is also the birthplace of the main rivers in Southeast Asia and plays an important strategic role. However, due to its remote location and complex topography, the observation of surface hydrometeorological elements is extremely scarce, which seriously restricts the understanding of the water cycle in this area. Using remote sensing images to extract rivers and lakes on TP can obtain a lot of valuable water resources information. However, the downloading and processing of remote sensing images is very time-consuming, especially the processing of remote sensing images with large-scale and long time series often involves hundreds of gigabytes of data, which requires a high level of personal computers and is inefficient. As a cloud platform dedicated to data processing and analysis of geoscience, Google Earth Engine(GEE) integrates many excellent remote sensing image processing algorithms. It does not need to download images and supports online remote sensing image processing, which greatly improves the output efficiency. Based on GEE, the monthly data of Yarlung Zangbo River at Nuxia Hydrological Station and the annual data of typical lakes were extracted and vectorized from the pre-processed Landsat series images. It was found that the area of Yarlung Zangbo River at Nuxia Hydrological Station varies periodically. The changing trend of typical lakes is also revealed.</p>
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44

Shujun, Liu. "Advances in Remote Sensing Extraction of Urban Roads." E3S Web of Conferences 290 (2021): 02023. http://dx.doi.org/10.1051/e3sconf/202129002023.

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As early as 1970s, the United States has begun the research of remote sensing image processing technology. In recent ten years, the research of road remote sensing extraction in China has also advanced by leaps and bounds. High resolution remote sensing images have been widely used in many fields, such as urban development planning, environmental monitoring and evaluation, and public announcement information services. The main application goal of remote sensing image is to extract the information of the object of interest, then identify it and complete the image understanding. Road is the most important and basic transportation mode of human beings, which provides a lot of support for the development of human civilization. road extraction is important for traffic management, including urban planning, road monitoring, GPS navigation, map updating, image registration, etc. extracting roads from high-resolution remote sensing satellite images is not only a challenging research direction, but also of great practical value.
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45

Bai, Hong Tao, Yu Gang Li, Li Ying Chen, and Yan Ling Wang. "Parallel Optimization of Geometric Correction Algorithm Based on CPU-GPU Hybrid Architecture." Applied Mechanics and Materials 543-547 (March 2014): 2804–8. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2804.

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Geometric correction is an essential processing procedure in remote sensing image processing. The algorithms used in geometric correction are time intensive and the size of remote sensing images is very large. Meanwhile,the data to be calculated is in huge size and is accumulating rapidly every day. Hence, the fast processing of geometric correction of remote sensing image becomes an urgent research problem. Through the rapid development of GPU, the current GPU has a great advantage in processing speed and memory bandwidth over CPU. It provides a new way for high performance computing. In this paper, we present three optimization solutions based on CPU-GPU hybrid architecture and the analysis of their performances. Experiments are also given and the results are consistent with the analysis.
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Lee, Jong Jae, Masanobu Shinozuka, and Soo Jin Cho. "Remote Sensing of Bridge Displacement Using Digital Image Processing Techniques." Key Engineering Materials 321-323 (October 2006): 404–9. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.404.

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In this study, an optical method of real-time displacement measurement of such bridges was carried out by means of digital image processing techniques. A commercially available digital video camera combined with a telescopic device takes a motion picture of the target panel with known geometry, which is installed on the measurement location of a bridge. The displacement of the target is calculated based on the captured images in real-time manner using image processing techniques, which require a texture recognition algorithm, projection of the captured image, and calculation of the actual displacement using target geometry and number of pixels moved. For the purpose of verification of the presented method, a laboratory test was made using shaking table test and the measured displacement by image processing techniques was compared with the data from a contact-type sensor, a linear variable differential transformer (LVDT). The proposed method gave close results to a conventional sensor. Field tests were carried out on a bridge with steel plate girders and a bridge with steel box girders. The test results gave sufficient dynamic resolution in frequency as well as the amplitude.
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47

Lu, Liang, Leng Zhang, and Ting Zhang. "JPEG2000-Based Optimization Algorithm for Effective Compression Display of Remote Sensing Images." Applied Mechanics and Materials 325-326 (June 2013): 1602–9. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1602.

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Taking the compression issue of remote sensing images as the study subject, this paper analyses the technical process for JPEG2000 compression and the image features of remote sensing images, natural images and figural images, and puts forwards an integrative optimization algorithm for effective compression display for remote sensing images based on common JPEG2000 compression frame. Thereinto, it includes high-frequency component filtering treatment, parallel processing of bit plane coding pass scanning and improved ROI coding algorithm, which give obvious clews to both overall consumed time for image compression and the gradual display effect in ROI region.
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48

Li, Haifeng, Xin Dou, Chao Tao, et al. "RSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data." Sensors 20, no. 6 (2020): 1594. http://dx.doi.org/10.3390/s20061594.

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Image classification is a fundamental task in remote sensing image processing. In recent years, deep convolutional neural networks (DCNNs) have experienced significant breakthroughs in natural image recognition. The remote sensing field, however, is still lacking a large-scale benchmark similar to ImageNet. In this paper, we propose a remote sensing image classification benchmark (RSI-CB) based on massive, scalable, and diverse crowdsourced data. Using crowdsourced data, such as Open Street Map (OSM) data, ground objects in remote sensing images can be annotated effectively using points of interest, vector data from OSM, or other crowdsourced data. These annotated images can, then, be used in remote sensing image classification tasks. Based on this method, we construct a worldwide large-scale benchmark for remote sensing image classification. This benchmark has large-scale geographical distribution and large total image number. It contains six categories with 35 sub-classes of more than 24,000 images of size 256 × 256 pixels. This classification system of ground objects is defined according to the national standard of land-use classification in China and is inspired by the hierarchy mechanism of ImageNet. Finally, we conduct numerous experiments to compare RSI-CB with the SAT-4, SAT-6, and UC-Merced data sets. The experiments show that RSI-CB is more suitable as a benchmark for remote sensing image classification tasks than other benchmarks in the big data era and has many potential applications.
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Zhao, Yu, Fan Feng Meng, and Jiang Feng. "Unmanned Aerial Vehicle Based Agricultural Remote Sensing Multispectral Image Processing Methods." Advanced Materials Research 905 (April 2014): 585–88. http://dx.doi.org/10.4028/www.scientific.net/amr.905.585.

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In order to provide more flexibility in remote sensing image collection, unmanned aerial vehicle has been used to kinds of agricultural productions. Images acquired from the UAV based RS system were very useful as a result of their high spatial resolution and low turn-around time. This paper discussed general methods to process the multispectral RS data at image process level. The distortion correction caused by sensor was introduced. The geometric distortion comprised sensor distortion and external distortion caused by external parameters. At last, the general image mosaic methods were discussed.
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Zeng, Guang Wei, Gui Fen Chen, Chu Nan Li, and Jiao Ye. "The Comparative Study of Remote Sensing Image Classification Method Based on ERDAS." Advanced Materials Research 546-547 (July 2012): 542–47. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.542.

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ERDAS IMAGINE is a remote sensing image processing system developed by the United States.The paper using ERDAS to classified the remote sensing of Land-sat TM image data by supervised classification method and unsupervised classification method, Using the Yushu City remote sensing image of Jilin Province as the trial data, and classified the forest, arable land and water from the remote sensing images, compared the test data of the supervised classification and unsupervised classification method, shows that the supervised classification method can be better to solute the questions "with the spectrum of foreign body" and "synonyms spectrum" than unsupervised classification method, and optimize classification images, improved information extraction accuracy. The application shows the classification result is consistent with the actual situation and it laid the foundation for land to have the rational planning and use.
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