Journal articles: 'Remote sensing - Mathematics'

1

Hügel, Max, Holger Rauhut, and Thomas Strohmer. "Remote Sensing via ℓ 1-Minimization." Foundations of Computational Mathematics 14, no. 1 (May 29, 2013): 115–50. http://dx.doi.org/10.1007/s10208-013-9157-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

MacBain, John, and Bruce Secrest. "Source Identification in Remote Sensing Problems." SIAM Review 33, no. 1 (March 1991): 109–13. http://dx.doi.org/10.1137/1033007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Carr, James R. "Spatial Statistics for Remote Sensing." Mathematical Geology 37, no. 5 (July 2005): 549–50. http://dx.doi.org/10.1007/s11004-005-6672-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Liu, Yun, and Jia-Bao Liu. "Abnormal Target Detection Method in Hyperspectral Remote Sensing Image Based on Convolution Neural Network." Computational Intelligence and Neuroscience 2022 (May 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/9223552.

Full text

Abstract:

Abnormal target detection in hyperspectral remote sensing image is one of the hotspots in image research. The image noise generated in the detection process will lead to the decline of the quality of hyperspectral remote sensing image. In view of this, this paper proposes an abnormal target detection method of hyperspectral remote sensing image based on the convolution neural network. Firstly, the deep residual learning network model has been used to remove the noise in hyperspectral remote sensing image. Secondly, the spatial and spectral features of hyperspectral remote sensing images were used to optimize the clustering dictionary, and then the image segmentation containing target information is completed. Finally, the image was input into the deep convolution neural network with a dual classifier, and the network detects the abnormal target in the image. The test results of this algorithm show that the structural similarity of the denoised image is higher than 0.86, which shows that this method has good noise reduction performance, image details will not damage, segmentation effect is good, and it can obtain high-definition target image information and accurately detect abnormal targets in the image.

APA, Harvard, Vancouver, ISO, and other styles

5

Xie, Huaming, Qianjiao Wu, Ting Zhang, Zhende Teng, Hao Huang, Ying Shu, Shaoru Feng, and Jing Lou. "A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images." Journal of Mathematics 2021 (December 14, 2021): 1–16. http://dx.doi.org/10.1155/2021/1860160.

Full text

Abstract:

In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production. It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies. Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy. Initially, the segmentation of the image is carried out. To this end, the parcel data is used to generate the region of interest (ROI) of each parcel. Second, the homogeneity of each ROI is detected by its statistical indices (mean value and standard deviation). Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous ROIs. Finally, two classification results are combined as the final classification result. The new algorithm was applied to a complex planting area of 103.60 km2 in central China based on the ownership parcel data and Gaofen-1 PMS and WFV remote sensing images in this paper. The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa = 0.9279, overall accuracy = 96.41%, user’s accuracy = 99.16%, producer’s accuracy = 93.39%, commission errors = 0.84%, and omission errors = 6.61%) when the size of ownership parcels matches the spatial resolution of remote sensing images.

APA, Harvard, Vancouver, ISO, and other styles

6

Zhen, Longxia, and Wei Liang. "Planning and Design Method of Multiangle Ecological Building Edge Space under the Background of Rural Revitalization." Mathematical Problems in Engineering 2022 (September 16, 2022): 1–9. http://dx.doi.org/10.1155/2022/2848164.

Full text

Abstract:

Under the background of rural revitalization, in order to realize the planning and design of ecological building edge space, a multi-perspective ecological building edge space planning and design method based on remote sensing image edge segmentation is proposed. The remote sensing visual detection of ecological buildings is realized by fusing multiscale features and multisource scene remote sensing images, and the extracted remote sensing image feature points are calibrated to extract the location information, texture features, super-resolution edge information features, and different levels of change features of the spatial distribution of the edge of ecological buildings. The background difference detection model of an ecological building remote sensing image is established, and the distance of the centroid of the corresponding level is calculated through frame dynamic planning and differential image clustering. Combined with the edge contour detection method of ecological building remote sensing image, the edge space planning and design are realized. The simulation results show that this method has higher accuracy in planning and better accuracy in detecting the contour of ecological building edge space and improves the dynamic planning and positioning ability of multi-perspective ecological building edge space distribution.

APA, Harvard, Vancouver, ISO, and other styles

7

Mamoshin, V. R., and V. I. Nemchinov. "Digital multitariff remote-sensing power supply meters." Measurement Techniques 42, no. 7 (July 1999): 680–82. http://dx.doi.org/10.1007/bf02512091.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Jordan, A. K., and M. E. Veysoglu. "Electromagnetic remote sensing of sea ice." Inverse Problems 10, no. 5 (October 1, 1994): 1041–58. http://dx.doi.org/10.1088/0266-5611/10/5/004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Li, Hongchao, and Fang Wu. "Conversion and Visualization of Remote Sensing Image Data in CAD." Computer-Aided Design and Applications 18, S3 (October 20, 2020): 82–94. http://dx.doi.org/10.14733/cadaps.2021.s3.82-94.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

10

Pemberton, Joseph C. "Solving Over-Constrained Satellite Remote-Sensing Scheduling Problems." Electronic Notes in Discrete Mathematics 4 (January 2000): 54. http://dx.doi.org/10.1016/s1571-0653(05)80105-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Chen, Chao, Hua Kong, and Bin Wu. "Edge detection of remote sensing image based on Grünwald-Letnikov fractional difference and Otsu threshold." Electronic Research Archive 31, no. 3 (2023): 1287–302. http://dx.doi.org/10.3934/era.2023066.

Full text

Abstract:

<abstract><p>With the development of remote sensing technology, the resolution of remote sensing images is improving, and the presentation of geomorphic information is becoming more and more abundant, the difficulty of identifying and extracting edge information is also increasing. This paper demonstrates an algorithm to detect the edges of remote sensing images based on Grünwald–Letnikov fractional difference and Otsu threshold. First, a convolution difference mask with two parameters in four directions is constructed by using the definition of the Grünwald–Letnikov fractional derivative. Then, the mask is convolved with the gray image of the remote sensing image, and the edge detection image is obtained by binarization with Otsu threshold. Finally, the influence of two parameters and threshold values on detection results is discussed. Compared with the results of other detectors on the NWPU VHR-10 dataset, it is found that the algorithm not only has good visual effect but also shows good performance in quantitative evaluation indicators (binary graph similarity and edge pixel ratio).</p></abstract>

APA, Harvard, Vancouver, ISO, and other styles

12

Mehmood, Maryam, Ahsan Shahzad, Bushra Zafar, Amsa Shabbir, and Nouman Ali. "Remote Sensing Image Classification: A Comprehensive Review and Applications." Mathematical Problems in Engineering 2022 (August 2, 2022): 1–24. http://dx.doi.org/10.1155/2022/5880959.

Full text

Abstract:

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

APA, Harvard, Vancouver, ISO, and other styles

13

Han, Yanling, Cong Wei, Ruyan Zhou, Zhonghua Hong, Yun Zhang, and Shuhu Yang. "Combining 3D-CNN and Squeeze-and-Excitation Networks for Remote Sensing Sea Ice Image Classification." Mathematical Problems in Engineering 2020 (April 7, 2020): 1–15. http://dx.doi.org/10.1155/2020/8065396.

Full text

Abstract:

Sea ice is one of the most prominent marine disasters in high latitudes. Remote sensing technology provides an effective means for sea ice detection. Remote sensing sea ice images contain rich spectral and spatial information. However, most traditional methods only focus on spectral information or spatial information, and do not excavate the feature of spectral and spatial simultaneously in remote sensing sea ice images classification. At the same time, the complex correlation characteristics among spectra and small sample problem in sea ice classification also limit the improvement of sea ice classification accuracy. For this issue, this paper proposes a new remote sensing sea ice image classification method based on squeeze-and-excitation (SE) network, convolutional neural network (CNN), and support vector machines (SVMs). The proposed method designs 3D-CNN deep network so as to fully exploit the spatial-spectrum features of remote sensing sea ice images and integrates SE-Block into 3D-CNN in-depth network in order to distinguish the contributions of different spectra to sea ice classification. According to the different contributions of spectral features, the weight of each spectral feature is optimized by fusing SE-Block in order to further enhance the sample quality. Finally, information-rich and representative samples are chosen by combining the idea of active learning and input into SVM classifier, and this achieves superior classification accuracy of remote sensing sea ice images with small samples. In order to verify the effectiveness of the proposed method, we conducted experiments on three different data from Baffin Bay, Bohai Bay, and Liaodong Bay. The experimental results show that compared with other classical classification methods, the proposed method comprehensively considers the correlation among spectral features and the small samples problems and deeply excavates the spatial-spectrum characteristics of sea ice and achieves better classification performance, which can be effectively applied to remote sensing sea ice image classification.

APA, Harvard, Vancouver, ISO, and other styles

14

He, Liheng, Tingru Zhu, and Meng Lv. "An Early Warning Intelligent Algorithm System for Forest Resource Management and Monitoring." Computational Intelligence and Neuroscience 2022 (October 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/4250462.

Full text

Abstract:

The development of remote sensing technology has passed an effective means for forest resource management and monitoring, but remote sensing technology is limited by sensor hardware equipment, and the quality of remote sensing image data is low, which is difficult to meet the needs of forest resource change monitoring. This paper presents a remote sensing image classification method based on the combination of the SSIF algorithm and wavelet denoising. Forest information is extracted from PALSAR/PALSAR-2 radar remote sensing data. The forest distribution map is generated by pixel level fusion algorithm, and the accuracy of the forest distribution map is evaluated by a confusion matrix. The remote sensing image is spatio-temporal fused by the SSIF algorithm to capture more details of forest distribution. The simulation analysis shows that the overall accuracy of the forest classification results obtained by the fusion algorithm is 96% ± 1, and the kappa coefficient is 0.66. The accuracy of forest recognition meets the requirements.

APA, Harvard, Vancouver, ISO, and other styles

15

Zeng, Wei. "Application of DTCWT Decomposition and Partial Differential Equation Denoising Methods in Remote Sensing Image Big Data Denoising and Reconstruction." Journal of Applied Mathematics 2022 (October 27, 2022): 1–13. http://dx.doi.org/10.1155/2022/8553330.

Full text

Abstract:

The precision of the traditional satellite remote sensing image denoising model cannot deal well with some precise production scenes. To solve this problem, this research proposes an improved remote sensing image processing model, in which the dual tree complex wavelet transform (DTCWT) method is used to conduct multiscale decomposition of the impact, and the fourth-order differential equation is used to denoise the decomposed complex high-frequency subband information, and then the denoised subbands are reconstructed into the denoised image. Through these two advanced signal-processing methods, the quality of reconstructed signals is improved and the noise content of various types is greatly reduced. The experimental results show that the normalized root mean square error of the denoising model designed in this study after training convergence is 0.02. When the noise variance is 0.030, the structure similarity, peak signal to noise ratio, and normalized signal to noise ratio are 0.74, 25.3, and 0.76, respectively, which are better than all other comparison models. The experimental data prove that the satellite remote sensing image data denoising model designed in this study has better denoising performance, and has certain application potential in high-precision satellite remote sensing image big data processing.

APA, Harvard, Vancouver, ISO, and other styles

16

Dhingra, Sakshi, and Dharminder Kumar. "A prologue to natural computing in remote sensing." Journal of Interdisciplinary Mathematics 23, no. 2 (February 17, 2020): 591–605. http://dx.doi.org/10.1080/09720502.2020.1731979.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

He, Zheng, Gang Ye, Hui Jiang, and Youming Fu. "Vehicle Emission Detection in Data-Driven Methods." Mathematical Problems in Engineering 2020 (October 13, 2020): 1–13. http://dx.doi.org/10.1155/2020/4875310.

Full text

Abstract:

Environmental protection is a fundamental policy in many countries, where the vehicle emission pollution turns to be outstanding as a main component of pollutions in environmental monitoring. Remote sensing technology has been widely used on vehicle emission detection recently and this is mainly due to the fast speed, reality, and large scale of the detection data retrieved from remote sensing methods. In the remote sensing process, the information about the fuel type and registration time of new cars and nonlocal registered vehicles usually cannot be accessed, leading to the failure in assessing vehicle pollution situations directly by analyzing emission pollutants. To handle this problem, this paper adopts data mining methods to analyze the remote sensing data to predict fuel type and registration time. This paper takes full use of decision tree, random forest, AdaBoost, XgBoost, and their fusion models to successfully make precise prediction for these two essential information and further employ them to an essential application: vehicle emission evaluation.

APA, Harvard, Vancouver, ISO, and other styles

18

Bagan, Hasi, Ram Avtar, Hajime Seya, and Huade Guan. "Mathematics in Utilizing Remote Sensing Data for Investigating and Modelling Environmental Problems." Mathematical Problems in Engineering 2017 (2017): 1–3. http://dx.doi.org/10.1155/2017/7430658.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Hua, Zhen, Zhenzhu Bian, and Jinjiang Li. "Airport Detection-Based Cosaliency on Remote Sensing Images." Mathematical Problems in Engineering 2021 (May 7, 2021): 1–17. http://dx.doi.org/10.1155/2021/8956396.

Full text

Abstract:

This paper proposes a contour extraction model based on cosaliency detection for remote sensing image airport detection and improves the traditional line segmentation detection (LSD) algorithm to make it more suitable for the goal of this paper. Our model consists of two parts, a cosaliency detection module and a contour extraction module. In the first part, the cosaliency detection module mainly uses the network framework of Visual Geometry Group-19 (VGG-19) to obtain the result maps of the interimage comparison and the intraimage consistency, and then the two result maps are multiplied pixel by pixel to obtain the cosaliency mask. In the second part, the contour extraction module uses superpixel segmentation and parallel line segment detection (PLSD) to refine the airport contour and runway information to obtain the preprocessed result map, and then we merge the result of cosaliency detection with the preprocessed result to obtain the final airport contour. We compared the model proposed in this article with four commonly used methods. The experimental results show that the accuracy of the model is 15% higher than that of the target detection result based on the saliency model, and the accuracy of the active contour model based on the saliency analysis is improved by 1%. This shows that the model proposed in this paper can extract a contour that closely matches the actual target.

APA, Harvard, Vancouver, ISO, and other styles

20

Zhang, Yiming, and Xiang Li. "Yolo-Based Improvements in Remote Sensing Image Applications." Mathematical Problems in Engineering 2022 (December 15, 2022): 1–15. http://dx.doi.org/10.1155/2022/1272896.

Full text

Abstract:

The identification of some specific targets in remote sensing images is still quite challenging despite the adequate accuracy of deep learning-based target detection models. This work proposes a variant of YOLOv3 based on the residual structure as the backbone and the attention mechanism module, which improves the ability of YOLOv3 to extract features. SGE is a lightweight module that can fully extract features from images without bringing an increase in computation. Furthermore, the dilated encoder module used in YOLOF was introduced as a neck to enrich the perceptual field of the C5 feature layer by concatenating four layers of dilated convolution with different expansion coefficients. The C5 feature layer and the residual structure were further processed to contain sufficient scale information for further detection. In terms of the mean average precision (mAP), experimental results demonstrate that the proposed model outperforms the other models: YOLOv3, faster-RCNN-r50+GACL Net, and YOLOv4.

APA, Harvard, Vancouver, ISO, and other styles

21

Shabbir, Amsa, Nouman Ali, Jameel Ahmed, Bushra Zafar, Aqsa Rasheed, Muhammad Sajid, Afzal Ahmed, and Saadat Hanif Dar. "Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50." Mathematical Problems in Engineering 2021 (July 12, 2021): 1–18. http://dx.doi.org/10.1155/2021/5843816.

Full text

Abstract:

Image classification has gained lot of attention due to its application in different computer vision tasks such as remote sensing, scene analysis, surveillance, object detection, and image retrieval. The primary goal of image classification is to assign the class labels to images according to the image contents. The applications of image classification and image analysis in remote sensing are important as they are used in various applied domains such as military and civil fields. Earlier approaches for remote sensing images and scene analysis are based on low-level feature representations such as color- and texture-based features. Vector of Locally Aggregated Descriptors (VLAD) and orderless Bag-of-Features (BoF) representations are the examples of mid-level approaches for remote sensing image classification. Recent trends for remote sensing and scene classification are focused on the use of Convolutional Neural Network (CNN). Keeping in view the success of CNN models, in this research, we aim to fine-tune ResNet50 by using network surgery and creation of network head along with the fine-tuning of hyperparameters. The learning of hyperparameters is tuned by using a linear decay learning rate scheduler known as piecewise scheduler. To tune the optimizer hyperparameter, Stochastic Gradient Descent with Momentum (SGDM) is used with the usage of weight learn and bias learn rate factor. Experiments and analysis are conducted on five different datasets, that is, UC Merced Land Use Dataset (UCM), RSSCN (the remote sensing scene classification image dataset), SIRI-WHU, Corel-1K, and Corel-1.5K. The analysis and competitive results exemplify that our proposed image classification-based model can classify the images in a more effective and efficient manner as compared to the state-of-the-art research.

APA, Harvard, Vancouver, ISO, and other styles

22

Wang, Min, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng, and Gang Liu. "A Unidirectional Total Variation and Second-Order Total Variation Model for Destriping of Remote Sensing Images." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/4397189.

Full text

Abstract:

Remote sensing images often suffer from stripe noise, which greatly degrades the image quality. Destriping of remote sensing images is to recover a good image from the image containing stripe noise. Since the stripes in remote sensing images have a directional characteristic (horizontal or vertical), the unidirectional total variation has been used to consider the directional information and preserve the edges. The remote sensing image contaminated by heavy stripe noise always has large width stripes and the pixels in the stripes have low correlations with the true pixels. On this occasion, the destriping process can be viewed as inpainting the wide stripe domains. In many works, high-order total variation has been proved to be a powerful tool to inpainting wide domains. Therefore, in this paper, we propose a variational destriping model that combines unidirectional total variation and second-order total variation regularization to employ the directional information and handle the wide stripes. In particular, the split Bregman iteration method is employed to solve the proposed model. Experimental results demonstrate the effectiveness of the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

23

Jin, Liang, and Guodong Liu. "An Approach on Image Processing of Deep Learning Based on Improved SSD." Symmetry 13, no. 3 (March 17, 2021): 495. http://dx.doi.org/10.3390/sym13030495.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

24

Guan, XianMing, Di Wang, Luhe Wan, and Jiyi Zhang. "Extracting Wetland Type Information with a Deep Convolutional Neural Network." Computational Intelligence and Neuroscience 2022 (May 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/5303872.

Full text

Abstract:

Wetlands have important ecological value. The application of wetland remote sensing is essential for the timely and accurate analysis of the current situation in wetlands and dynamic changes in wetland resources, but high-resolution remote sensing images display nonobvious boundaries between wetland types. However, high classification accuracy and time efficiency cannot be guaranteed simultaneously. Extraction of wetland type information based on high-spatial-resolution remote sensing images is a bottleneck that has hindered wetland development research and change detection. This paper proposes an automatic and efficient method for extracting wetland type information. First, the object-oriented multiscale segmentation method is used to realize the fine segmentation of high-resolution remote sensing images, and then the deep convolutional neural network model AlexNet is used to classify automatically the types of wetland images. The method is verified in a case study involving field-measured data, and the classification results are compared with those of traditional classification methods. The results show that the proposed method can more accurately and efficiently extract different wetland types in high-resolution remote sensing images than the traditional classification methods. The proposed method will be helpful in the extension and application of wetland remote sensing technology and will provide technical support for the protection, development, and utilization of wetland resources.

APA, Harvard, Vancouver, ISO, and other styles

25

McGonigle, Andrew J. S. "Volcano remote sensing with ground-based spectroscopy." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 363, no. 1837 (October 20, 2005): 2915–29. http://dx.doi.org/10.1098/rsta.2005.1668.

Full text

Abstract:

The chemical compositions and emission rates of volcanic gases carry important information about underground magmatic and hydrothermal conditions, with application in eruption forecasting. Volcanic plumes are also studied because of their impacts upon the atmosphere, climate and human health. Remote sensing techniques are being increasingly used in this field because they provide real-time data and can be applied at safe distances from the target, even throughout violent eruptive episodes. However, notwithstanding the many scientific insights into volcanic behaviour already achieved with these approaches, technological limitations have placed firm restrictions upon the utility of the acquired data. For instance, volcanic SO 2 emission rate measurements are typically inaccurate (errors can be greater than 100%) and have poor time resolution ( ca once per week). Volcanic gas geochemistry is currently being revolutionized by the recent implementation of a new generation of remote sensing tools, which are overcoming the above limitations and are providing degassing data of unprecedented quality. In this article, I review this field at this exciting point of transition, covering the techniques used and the insights thereby obtained, and I speculate upon the breakthroughs that are now tantalizingly close.

APA, Harvard, Vancouver, ISO, and other styles

26

Et.al, P. Devi. "Remote Monitoring And Localization: Tools For Smart Parking." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 3985–90. http://dx.doi.org/10.17762/turcomat.v12i3.1688.

Full text

Abstract:

The increase in usage of IoT environment is beyond the limits, which use Wireless (WSN).These networks can be used in many environments like logistics, supply chain management,health management,e-governance, smart parking, smart city, and smart appliances. WSN is a collection of sensors spread across them to base station/sink. Remote sensing sensors do sensing of an object remotely and detectthe static or dynamic information.In this paper, discussion is made on how to monitor and track the person’scar within a shopping mall. Remote monitoring and local optimization techniques are used in smart parking architecture, which suits for smart parking environment. Further, description of some of the monitoring and tracking techniques which was used earlier also been discussed with different types of protocols used for this appropriate environment. These systems can be implemented for effective smart parking.

APA, Harvard, Vancouver, ISO, and other styles

27

Zou, Quan, Wenyang Yu, and Guoqing Li. "An Efficient Hierarchical Representation Approach of Remote Sensing Application Modeling Based on Distributed Environment." Mathematical Problems in Engineering 2020 (December 3, 2020): 1–14. http://dx.doi.org/10.1155/2020/4684963.

Full text

Abstract:

In Earth science, information science, space science, and other disciplines, scientists use the land surface parameter inversion method in their work, applying this to the atmosphere, vegetation, soil, drought, and so on. Multidisciplinary experts sometimes collaborate on a particular application. However, these remote sensing models do not have a unified method of description and management and cannot effectively achieve the sharing of models and data resources. It is also hard to meet user demand for global data and models in the current state, especially in the face of global problems and long-term series problems. In this paper, we examine the scientific questions of the computability and scalability of remote sensing models. This paper adopts a data dependency approach to describe a remote sensing model and implements a hierarchical unified description and management method using modelling based on four layers: a data-processing view, an atomic model view, an on-demand resource package view, and a workflow view. We choose three typical remote sensing models for disaster monitoring as use cases and describe the practical application process of the proposed method. The results demonstrate the advantages and powerful capabilities of this efficient method.

APA, Harvard, Vancouver, ISO, and other styles

28

Xia, Bin, Fanyu Kong, Jun Zhou, Xin Wu, and Qiong Xie. "Land Resource Use Classification Using Deep Learning in Ecological Remote Sensing Images." Computational Intelligence and Neuroscience 2022 (April 21, 2022): 1–9. http://dx.doi.org/10.1155/2022/7179477.

Full text

Abstract:

Aiming at the problems that the traditional remote sensing image classification methods cannot effectively integrate a variety of deep learning features and poor classification performance, a land resource use classification method based on a convolutional neural network (CNN) in ecological remote sensing images is proposed. In this study, a seven-layer convolution neural network is constructed, and then the two fully connected layer features of the improved CNN network training output are fused with the fifth layer pooled layer features after dimensionality reduction by principal component analysis (PCA), so as to obtain an effective remote sensing image feature of land resources based on deep learning. Further, the classification of land resources remote sensing images is completed based on a support vector machine classifier. The remote sensing images of Pingshuo mining area in Shanxi Province are used to analyze the proposed method. The results show that the edge of the recognized image is clear, the classification accuracy, misclassification rate, and kappa coefficient are 0.9472, 0.0528, and 0.9435, respectively, and the model has excellent overall performance and good classification effect.

APA, Harvard, Vancouver, ISO, and other styles

29

Wulamu, Aziguli, Zuxian Shi, Dezheng Zhang, and Zheyu He. "Multiscale Road Extraction in Remote Sensing Images." Computational Intelligence and Neuroscience 2019 (July 10, 2019): 1–9. http://dx.doi.org/10.1155/2019/2373798.

Full text

Abstract:

Recent advances in convolutional neural networks (CNNs) have shown impressive results in semantic segmentation. Among the successful CNN-based methods, U-Net has achieved exciting performance. In this paper, we proposed a novel network architecture based on U-Net and atrous spatial pyramid pooling (ASPP) to deal with the road extraction task in the remote sensing field. On the one hand, U-Net structure can effectively extract valuable features; on the other hand, ASPP is able to utilize multiscale context information in remote sensing images. Compared to the baseline, this proposed model has improved the pixelwise mean Intersection over Union (mIoU) of 3 points. Experimental results show that the proposed network architecture can deal with different types of road surface extraction tasks under various terrains in Yinchuan city, solve the road connectivity problem to some extent, and has certain tolerance to shadows and occlusion.

APA, Harvard, Vancouver, ISO, and other styles

30

Evans, D. R. "A microwave transceiver for remote sensing of high voltages." Measurement Science and Technology 2, no. 7 (July 1, 1991): 679–81. http://dx.doi.org/10.1088/0957-0233/2/7/018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Bushmeleva, K. I., I. I. Plyusnin, P. E. Bushmelev, and S. U. Uvaisov. "Modeling the optimal parameters for a remote sensing device." Measurement Techniques 54, no. 3 (June 2011): 294–99. http://dx.doi.org/10.1007/s11018-011-9723-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Belov, N. N. "Remote sensing of aerosol parameters from laser breakdown characteristics." Measurement Techniques 34, no. 9 (September 1991): 912–16. http://dx.doi.org/10.1007/bf00980802.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Wang, Yu, Xiaofei Wang, and Junfan Jian. "Remote Sensing Landslide Recognition Based on Convolutional Neural Network." Mathematical Problems in Engineering 2019 (September 18, 2019): 1–12. http://dx.doi.org/10.1155/2019/8389368.

Full text

Abstract:

Landslides are a type of frequent and widespread natural disaster. It is of great significance to extract location information from the landslide in time. At present, most articles still select single band or RGB bands as the feature for landslide recognition. To improve the efficiency of landslide recognition, this study proposed a remote sensing recognition method based on the convolutional neural network of the mixed spectral characteristics. Firstly, this paper tried to add NDVI (normalized difference vegetation index) and NIRS (near-infrared spectroscopy) to enhance the features. Then, remote sensing images (predisaster and postdisaster images) with same spatial information but different time series information regarding landslide are taken directly from GF-1 satellite as input images. By combining the 4 bands (red + green + blue + near-infrared) of the prelandslide remote sensing images with the 4 bands of the postlandslide images and NDVI images, images with 9 bands were obtained, and the band values reflecting the changing characteristics of the landslide were determined. Finally, a deep learning convolutional neural network (CNN) was introduced to solve the problem. The proposed method was tested and verified with remote sensing data from the 2015 large-scale landslide event in Shanxi, China, and 2016 large-scale landslide event in Fujian, China. The results showed that the accuracy of the method was high. Compared with the traditional methods, the recognition efficiency was improved, proving the effectiveness and feasibility of the method.

APA, Harvard, Vancouver, ISO, and other styles

34

Mei, Yong, Hao Chen, and Shuting Yang. "Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery." Algorithms 14, no. 10 (October 19, 2021): 300. http://dx.doi.org/10.3390/a14100300.

Full text

Abstract:

High-resolution remote sensing image building target detection has wide application value in the fields of land planning, geographic monitoring, smart cities and other fields. However, due to the complex background of remote sensing imagery, some detailed features of building targets are less distinguishable from the background. When carrying out the detection task, it is prone to problems such as distortion and the missing of the building outline. To address this challenge, we developed a novel building target detection method. First, a building detection method based on rectangular approximation and region growth was proposed, and a saliency detection model based on the foreground compactness and local contrast of manifold ranking is used to obtain the saliency map of the building region. Then, the boundary prior saliency detection method based on the improved manifold ranking algorithm was proposed for the target area of buildings with low contrast with the background in remote sensing imagery. Finally, fusing the results of the rectangular approximation-based and saliency-based detection, the proposed fusion method improved the Recall and F1 value of building detection, indicating that this paper provides an effective and efficient high-resolution remote sensing image building target detection method.

APA, Harvard, Vancouver, ISO, and other styles

35

SHANKAR, B. UMA, SAROJ K. MEHER, and ASHISH GHOSH. "NEURO-WAVELET CLASSIFIER FOR MULTISPECTRAL REMOTE SENSING IMAGES." International Journal of Wavelets, Multiresolution and Information Processing 05, no. 04 (July 2007): 589–611. http://dx.doi.org/10.1142/s0219691307001914.

Full text

Abstract:

A neuro-wavelet supervised classifier is proposed for land cover classification of multispectral remote sensing images. Features extracted from the original pixels information using wavelet transform (WT) are fed as input to a feed forward multi-layer neural network (MLP). The WT basically provides the spatial and spectral features of a pixel along with its neighbors and these features are used for improved classification. For testing the performance of the proposed method, we have used two IRS-1A satellite images and one SPOT satellite image. Results are compared with those of the original spectral feature based classifiers and found to be consistently better. Simulation study revealed that Biorthogonal 3.3 (Bior3.3) wavelet in combination with MLP performed better compared to all other wavelets. Results are evaluated visually and quantitatively with two measurements, β index of homogeneity and Davies–Bouldin (DB) index for compactness and separability of classes. We suggested a modified β index in accessing the percentage of accuracy (PAβ) of the classified images also.

APA, Harvard, Vancouver, ISO, and other styles

36

Deng, Yi, Chengyue Xing, and Ling Cai. "Building Image Feature Extraction Using Data Mining Technology." Computational Intelligence and Neuroscience 2022 (April 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/8006437.

Full text

Abstract:

At present, data mining technology is continuously researched in science and application. With the rapid development of remote sensing satellite industry, especially the launch of remote sensing satellites with high-resolution sensors, the amount of information obtained from remote sensing images has increased dramatically, which has largely promoted the application of remote sensing data in various industries. This technique mines useable information from less complete and accurate data while ensuring low program complexity. In order to determine the impact of data mining techniques on feature extraction of graphic images, this paper explores the relevant steps in the image recognition process, especially the image preenhancement and image extraction processes. This paper develops a preliminary set of relevant data and investigates two different extraction methods based on the availability or absence of nursing information. Aiming at the advantages and disadvantages of the two house extraction methods, this work discusses how to effectively integrate remote sensing data. It uses different data sources to describe different characteristics of buildings, analyzes and extracts effective information, and finally derives building information. The research results show that, using the SVM algorithm in data mining for image feature extraction, in the verified filtering window, the accuracy can be effectively improved by about 20%. Buildings are important objects in high-resolution remote sensing images, and their feature extraction and recognition technology is of great significance in many fields such as digital city construction, urban planning, and military reconnaissance.

APA, Harvard, Vancouver, ISO, and other styles

37

Braverman, Amy, Eric Fetzer, Annmarie Eldering, Silvia Nittel, and Kelvin Leung. "Semi-Streaming Quantization for Remote Sensing Data." Journal of Computational and Graphical Statistics 12, no. 4 (December 2003): 759–80. http://dx.doi.org/10.1198/1061860032535.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Ennouri, Karim, and Abdelaziz Kallel. "Remote Sensing: An Advanced Technique for Crop Condition Assessment." Mathematical Problems in Engineering 2019 (July 17, 2019): 1–8. http://dx.doi.org/10.1155/2019/9404565.

Full text

Abstract:

Actually, cultivators are increasingly arranging innovative high technical and scientific estimations in the aim to enhance agricultural sustainability, effectiveness, and/or plant health. Innovative farming technologies incorporate biology with smart agriculture: computers and devices exchange with one another autonomously in a structured farm management system. Throughout this structure, smart agriculture can be accomplished; cultivators decrease plantation inputs (pesticides and fertilizers) and increase yields via integrated pest management and/or biological control. The emerging concept of remote sensing may provide a framework to systematically consider these issues of smart farming technology and to embed high-tech agriculture better. The impact(s) may be beneficial depending on how tools, such as data mining, and imagery technologies, such as picture treatment and analysis, are applied. Remote sensing technology is discussed in this review and demonstrates its possibility to create novel opportunities for scientists (and agronomists) to explore aspects of biological phenomena that cannot be accessed through usual mechanisms or processes.

APA, Harvard, Vancouver, ISO, and other styles

39

Wang, Qunming, Xiaohua Tong, and Peter M. Atkinson. "A Geostatistical Filter for Remote Sensing Image Enhancement." Mathematical Geosciences 52, no. 3 (October 10, 2019): 317–36. http://dx.doi.org/10.1007/s11004-019-09829-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Lu, Guanyao, Dan Xu, and Yue Meng. "Dynamic Evolution Analysis of Desertification Images Based on BP Neural Network." Computational Intelligence and Neuroscience 2022 (March 17, 2022): 1–11. http://dx.doi.org/10.1155/2022/5645535.

Full text

Abstract:

In recent years, with the rise of artificial intelligence, deep neural network models have been used in various image recognition researches. Land desertification is a major environmental problem facing the world at present, and how to do a good job in dynamic monitoring is particularly important. For remote sensing images, this paper constructs a GA-PSO-BP analysis model based on BP neural network, genetic algorithm, and particle swarm algorithm and compares the classification training accuracies of the four models of BP, GA-BP, PSO-BP, and GA-PSO-BP; GA-PSO-BP was selected for dynamic analysis of desertification images, and the results showed the following: (1) By comparing the regional classification training accuracies of the four models of BP, GA-BP, PSO-BP, and GA-PSO-BP, the GA-PSO-BP neural network remote sensing image classification method proposed in this paper is simple and easy to operate. Compared with traditional remote sensing image classification methods and traditional neural network classification methods, the classification accuracy of remote sensing effects is improved. (2) Carrying out desertification analysis on remote sensing images of Horqin area, from 2010 to 2015, the desertified land area in the test area increased by 1.56 km2; from 2015 to 2020, the desertified land area in the test area decreased by 1.131 km2, and the desertified land in the test area from 2010 to 2020 showed a trend of increasing first and then decreasing, which is consistent with the actual situation. The GA-PSO-BP remote sensing image classification model has a good performance portability.

APA, Harvard, Vancouver, ISO, and other styles

41

LI, SHUTAO. "MULTISENSOR REMOTE SENSING IMAGE FUSION USING STATIONARY WAVELET TRANSFORM: EFFECTS OF BASIS AND DECOMPOSITION LEVEL." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 01 (January 2008): 37–50. http://dx.doi.org/10.1142/s0219691308002203.

Full text

Abstract:

Stationary wavelet transform is an efficient algorithm for remote sensing image fusion. In this paper, we investigate the effects of orthogonal/biorthogonal filters and decomposition depth on using stationary wavelet analysis for fusion. Spectral discrepancy and spatial distortion are used as quality measures. Empirical results lead to some recommendations on the wavelet filter parameters for use in remote sensing image fusion applications.

APA, Harvard, Vancouver, ISO, and other styles

42

Li, Lianying, Xi Chen, and Lianchao Li. "A Method for Extracting Building Information from Remote Sensing Images Based on Deep Learning." Computational Intelligence and Neuroscience 2022 (October 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/9968665.

Full text

Abstract:

Semantic segmentation of remote sensing images is an important issue in remote sensing tasks. Existing algorithms can extract information more accurately, but it is difficult to capture the contours of objects and further reveal the interaction information between different objects in the image. Therefore, a deep learning-based method for extracting building information from remote sensing images is proposed. First, the deep learning semantic segmentation model DeepLabv3+ and Mixconv2d are combined, and convolution kernels of different sizes are used for feature recognition. Then, the regularization method based on Rdrop Loss improves the accuracy and efficiency of contour capture for objects of different resolutions, and at the same time improves the consistency of dataset fitting. Finally, the proposed remote sensing image information extraction method is verified based on the self-built dataset. The experimental results show that the proposed algorithm can effectively improve the algorithm efficiency and result accuracy, and has good segmentation performance.

APA, Harvard, Vancouver, ISO, and other styles

43

ElKabbash, Mohamed, Kandammathe Valiyaveedu Sreekanth, Arwa Fraiwan, Jonathan Cole, Yunus Alapan, Theodore Letsou, Nathaniel Hoffman, et al. "Ultrathin-film optical coating for angle-independent remote hydrogen sensing." Measurement Science and Technology 31, no. 11 (September 9, 2020): 115201. http://dx.doi.org/10.1088/1361-6501/ab9fd8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

44

Yang, Weiwei, Haifeng Song, Lei Du, Songsong Dai, and Yingying Xu. "A Change Detection Method for Remote Sensing Images Based on Coupled Dictionary and Deep Learning." Computational Intelligence and Neuroscience 2022 (January 17, 2022): 1–14. http://dx.doi.org/10.1155/2022/3404858.

Full text

Abstract:

With the rapid development of remote sensing technology, change detection (CD) methods based on remote sensing images have been widely used in land resource planning, disaster monitoring, and urban expansion, among other fields. The purpose of CD is to accurately identify changes on the Earth’s surface. However, most CD methods focus on changes between the pixels of multitemporal remote sensing image pairs while ignoring the coupled relationships between them. This often leads to uncertainty about edge pixels with regard to changing objects and misclassification of small objects. To solve these problems, we propose a CD method for remote sensing images that uses a coupled dictionary and deep learning. The proposed method realizes the spatial-temporal modeling and correlation of multitemporal remote sensing images through a coupled dictionary learning module and ensures the transferability of reconstruction coefficients between multisource image blocks. In addition, we constructed a differential feature discriminant network to calculate the dissimilarity probability for the change area. A new loss function that considers true/false discrimination loss, classification loss, and cross-entropy loss is proposed. The most discriminating features can be extracted and used for CD. The performance of the proposed method was verified on two well-known CD datasets. Extensive experimental results show that the proposed method is superior to other methods in terms of precision, recall, F1-score, IoU , and OA .

APA, Harvard, Vancouver, ISO, and other styles

45

Foody, G. M. "Editorial: Ecological applications of remote sensing and GIS." Ecological Informatics 2, no. 2 (June 2007): 71–72. http://dx.doi.org/10.1016/j.ecoinf.2007.06.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

46

Chang, Ni-Bin. "Advances of ecological remote sensing under global change." Ecological Informatics 5, no. 5 (September 2010): 317. http://dx.doi.org/10.1016/j.ecoinf.2010.07.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

de Klerk, H. M., N. D. Burgess, and V. Visser. "Probabilistic description of vegetation ecotones using remote sensing." Ecological Informatics 46 (July 2018): 125–32. http://dx.doi.org/10.1016/j.ecoinf.2018.06.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Richards, Joseph W., Johanna Hardin, and Eric B. Grosfils. "Weighted model-based clustering for remote sensing image analysis." Computational Geosciences 14, no. 1 (April 16, 2009): 125–36. http://dx.doi.org/10.1007/s10596-009-9136-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Ling, Min, Qun Cheng, Jun Peng, Chenyi Zhao, and Ling Jiang. "Image Semantic Segmentation Method Based on Deep Learning in UAV Aerial Remote Sensing Image." Mathematical Problems in Engineering 2022 (April 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/5983045.

Full text

Abstract:

The existing semantic segmentation methods have some shortcomings in feature extraction of remote sensing images. Therefore, an image semantic segmentation method based on deep learning in UAV aerial remote sensing images is proposed. First, original remote sensing images obtained by S185 multirotor UAV are divided into smaller image blocks through sliding window and normalized to provide high-quality image set for subsequent operations. Then, the symmetric encoding-decoding network structure is improved. Bottleneck layer with 1 × 1 convolution is introduced to build ISegNet network model, and pooling index and convolution are used to fuse semantic information and image features. The improved encoding-decoding network gradually strengthens the extraction of details and reduces the number of parameters. Finally, based on ISegNet network, five-classification problem is transformed into five binary classification problems for network training, so as to obtain high-precision image semantic segmentation results. The experimental analysis of the proposed method based on TensorFlow framework shows that the accuracy value reaches 0.901, and the F1 value is not less than 0.83. The overall segmentation effect is better than those of other comparison methods.

APA, Harvard, Vancouver, ISO, and other styles

50

Dong, Rongsheng, Lulu Bai, and Fengying Li. "SiameseDenseU-Net-based Semantic Segmentation of Urban Remote Sensing Images." Mathematical Problems in Engineering 2020 (March 23, 2020): 1–14. http://dx.doi.org/10.1155/2020/1515630.

Full text

Abstract:

Boundary pixel blur and category imbalance are common problems that occur during semantic segmentation of urban remote sensing images. Inspired by DenseU-Net, this paper proposes a new end-to-end network—SiameseDenseU-Net. First, the network simultaneously uses both true orthophoto (TOP) images and their corresponding normalized digital surface model (nDSM) as the input of the network structure. The deep image features are extracted in parallel by downsampling blocks. Information such as shallow textures and high-level abstract semantic features are fused throughout the connected channels. The features extracted by the two parallel processing chains are then fused. Finally, a softmax layer is used to perform prediction to generate dense label maps. Experiments on the Vaihingen dataset show that SiameseDenseU-Net improves the F1-score by 8.2% and 7.63% compared with the Hourglass-ShapeNetwork (HSN) model and with the U-Net model. Regarding the boundary pixels, when using the same focus loss function based on median frequency balance weighting, compared with the original DenseU-Net, the small-target “car” category F1-score of SiameseDenseU-Net improved by 0.92%. The overall accuracy and the average F1-score also improved to varying degrees. The proposed SiameseDenseU-Net is better at identifying small-target categories and boundary pixels, and it is numerically and visually superior to the contrast model.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Remote sensing - Mathematics'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles