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Journal articles on the topic 'Remote-sensing images. Computer vision'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

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 correspondi

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Bass, L. P., Yu A. Plastinin, and I. Yu Skryabysheva. "The machine training in problems of satellite images’s processing." Metrologiya, no. 4 (2020): 15–37. http://dx.doi.org/10.32446/0132-4713.2020-4-15-37.

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Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are pr

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Chen, Guobin, and Wei Dai. "A Method of Restoring Fuzzy Remote Sensing Image Based on Dark Pixel Prior." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 08 (2019): 2054020. http://dx.doi.org/10.1142/s0218001420540208.

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Remote sensing image deblurring is a long-term and challenging inverse problem. Among them, the ability to find the correct image prior is the key to recovering high-quality and clear images. Therefore, in order to recover high-quality clear images, this paper has found a new and effective image prior: The dark pixel a priori in remote sensing images and a fuzzy remote sensing image restoration method based on dark pixel prior is proposed. Since the dark pixels in the clear remote sensing image will increase the pixel value of the dark pixels in the blurred remote sensing image due to the weig

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da Silva, C. C. V., K. Nogueira, H. N. Oliveira, and J. A. dos Santos. "TOWARDS OPEN-SET SEMANTIC SEGMENTATION OF AERIAL IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3/W2-2020 (October 29, 2020): 19–24. http://dx.doi.org/10.5194/isprs-annals-iv-3-w2-2020-19-2020.

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Abstract. Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images. However the development of pattern recognition approaches for these data is relatively recent, mainly due to the limited availability of this type of images, as until recently they were used exclusively for military purposes. Access to aerial imagery, including spectral information, has been increa

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Zhang, Yong Mei, and Li Ma. "Target Fusion Algorithm for Remote Sensing Image Recognition." Applied Mechanics and Materials 128-129 (October 2011): 1075–78. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1075.

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Multisource image fusion technology is widely applied in a variety of fields such as remote sensing, computer vision, medical diagnosis and military surveillance. In most instances, multi-spectral and panchromatic images can provide more complementary information for feature extraction. Two kinds of images are used for target recognition. Algorithms based on gradient for image fusion only consider high-frequency information changes of the images, and neglect the richness of high-frequency information. To solve this problem, a new kind of self-adaptive rule and algorithm based on gradient and e

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Zhou, Liming, Chang Zheng, Haoxin Yan, et al. "Vehicle Detection in Remote Sensing Image Based on Machine Vision." Computational Intelligence and Neuroscience 2021 (August 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/8683226.

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Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people’s hope. In this paper, a target detection algorithm named IR-PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR-

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Wang, Chisheng, Junzhuo Ke, Wenqun Xiu, Kai Ye, and Qingquan Li. "Emergency Response Using Volunteered Passenger Aircraft Remote Sensing Data: A Case Study on Flood Damage Mapping." Sensors 19, no. 19 (2019): 4163. http://dx.doi.org/10.3390/s19194163.

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Current satellite remote sensing data still have some inevitable defects, such as a low observing frequency, high cost and dense cloud cover, which limit the rapid response to ground changes and many potential applications. However, passenger aircraft may be an alternative remote sensing platform in emergency response due to the high revisit rate, dense coverage and low cost. This paper introduces a volunteered passenger aircraft remote sensing method (VPARS) for emergency response. It uses the images captured by the passenger volunteers during flight. Based on computer vision algorithms and g

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Schmitt, M., and Y. L. Wu. "REMOTE SENSING IMAGE CLASSIFICATION WITH THE SEN12MS DATASET." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2021 (June 17, 2021): 101–6. http://dx.doi.org/10.5194/isprs-annals-v-2-2021-101-2021.

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Abstract. Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene classification in remote sensing. However, in contrast to the computer vision community that has long been using well-established, large-scale standard datasets to train and benchmark high-capacity models, the remote sensing community still largely relies on relatively small and often application-dependend datasets, thus lacking comparability. With this paper, we present a classification-oriented conversio

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Dong, Yunyun, Weili Jiao, Tengfei Long, et al. "Local Deep Descriptor for Remote Sensing Image Feature Matching." Remote Sensing 11, no. 4 (2019): 430. http://dx.doi.org/10.3390/rs11040430.

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Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different

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Feng, R., X. Li, and H. Shen. "MOUNTAINOUS REMOTE SENSING IMAGES REGISTRATION BASED ON IMPROVED OPTICAL FLOW ESTIMATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 479–84. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-479-2019.

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<p><strong>Abstract.</strong> Mountainous remote sensing images registration is more complicated than in other areas as geometric distortion caused by topographic relief, which could not be precisely achieved via constructing local mapping functions in the feature-based framework. Optical flow algorithm estimating motion of consecutive frames in computer vision pixel by pixel is introduced for mountainous remote sensing images registration. However, it is sensitive to land cover changes that are inevitable for remote sensing image, resulting in incorrect displacement. To addr

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Bonnefon, R., P. Dhérété, and J. Desachy. "Geographic information system updating using remote sensing images." Pattern Recognition Letters 23, no. 9 (2002): 1073–83. http://dx.doi.org/10.1016/s0167-8655(02)00054-5.

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Li, Yangyang, Shuangkang Fang, Licheng Jiao, Ruijiao Liu, and Ronghua Shang. "A Multi-Level Attention Model for Remote Sensing Image Captions." Remote Sensing 12, no. 6 (2020): 939. http://dx.doi.org/10.3390/rs12060939.

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The task of image captioning involves the generation of a sentence that can describe an image appropriately, which is the intersection of computer vision and natural language. Although the research on remote sensing image captions has just started, it has great significance. The attention mechanism is inspired by the way humans think, which is widely used in remote sensing image caption tasks. However, the attention mechanism currently used in this task is mainly aimed at images, which is too simple to express such a complex task well. Therefore, in this paper, we propose a multi-level attenti

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Zeng, Yu Ju, and Ming Hui Chen. "Linear Feature Detectionin Images." Applied Mechanics and Materials 651-653 (September 2014): 2331–34. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.2331.

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Linear feature detection in digital images is an important low-level operationin computer vision that has many applications. In remote sensing tasks, it can be usedto extract roads, railroads, and rivers from satellite or low-resolution aerialimages,which can be used for the capture or update of data for geographic information andnavigation systems. In addition, it is useful in medical imaging for the extraction ofblood vessels from an X-ray angiography or the bones in the skull from a CT or MRimage. It also can be applied in horticulture for underground plant root detection inminirhizotron im

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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 s

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You, Yongfa, Siyuan Wang, Yuanxu Ma, et al. "Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index." Remote Sensing 10, no. 8 (2018): 1287. http://dx.doi.org/10.3390/rs10081287.

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Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing and computer vision. However, many irrelevant objects with similar spectral characteristics to buildings will cause a large amount of interference to the detection of buildings, thus making the accurate detection of buildings still a challenging task, especially for images captured in complex environments. Therefore, it is crucial to develop a method that can effectively eliminate these interferences and accurately detect buildings from complex image scenes. To this

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Khoshboresh-Masouleh, M., and R. Shah-Hosseini. "DEEP FEW-SHOT LEARNING FOR BI-TEMPORAL BUILDING CHANGE DETECTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-M-3-2021 (August 10, 2021): 99–103. http://dx.doi.org/10.5194/isprs-archives-xliv-m-3-2021-99-2021.

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Abstract. In real-world applications (e.g., change detection), annotating images is very expensive. To build effective deep learning models in these applications, deep few-shot learning methods have been developed and prove to be a robust approach in small training data. The study of building change detection from high spatial resolution satellite observations is important to research in remote sensing, photogrammetry, and computer vision nowadays, which can be widely used in a variety of real-world applications, such as map generation and updating. As manual high-resolution image interpretati

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Nadir Kurnaz, Mehmet, Zümray Dokur, and Tamer Ölmez. "Segmentation of remote-sensing images by incremental neural network." Pattern Recognition Letters 26, no. 8 (2005): 1096–104. http://dx.doi.org/10.1016/j.patrec.2004.10.004.

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Zhao, Cai. "Research on Multiband Packet Fusion Algorithm for Hyperspectral Remote Sensing Images." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 1 (2019): 153–57. http://dx.doi.org/10.20965/jaciii.2019.p0153.

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The data recorded by current algorithms contains more errors, which reduces the quality of hyperspectral remote sensing images and affects the fusion results. A fusion algorithm based on improved IHS transform is proposed. In order to avoid the noise and diffusion spread and the uniform distribution of gray level, the detail information is preserved and the image is geometric corrected, denoised and histogram equalized. Then the feature extraction, edge detection and feature matching are performed to the images. The weighted average fusion criterion is used to improve the fusion algorithm of I

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Wu, Z., X. Chen, Y. Gao, and Y. Li. "RAPID TARGET DETECTION IN HIGH RESOLUTION REMOTE SENSING IMAGES USING YOLO MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1915–20. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1915-2018.

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Object detection in high resolution remote sensing images is a fundamental and challenging problem in the field of remote sensing imagery analysis for civil and military application due to the complex neighboring environments, which can cause the recognition algorithms to mistake irrelevant ground objects for target objects. Deep Convolution Neural Network(DCNN) is the hotspot in object detection for its powerful ability of feature extraction and has achieved state-of-the-art results in Computer Vision. Common pipeline of object detection based on DCNN consists of region proposal, CNN feature

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Zhou, Liming, Haoxin Yan, Yingzi Shan, et al. "Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks." Journal of Electrical and Computer Engineering 2021 (August 11, 2021): 1–16. http://dx.doi.org/10.1155/2021/4685644.

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Aircraft detection for remote sensing images, as one of the fields of computer vision, is one of the significant tasks of image processing based on deep learning. Recently, many high-performance algorithms for aircraft detection have been developed and applied in different scenarios. However, the proposed algorithms still have a series of problems; for instance, the algorithms will miss some small-scale aircrafts when applied to the remote sensing image. There are two main reasons for the problem; one reason is that the aircrafts in the remote sensing image are usually small in size, leading t

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Han, Xiaofeng, Tao Jiang, Zifei Zhao, and Zhongteng Lei. "Research on Remote Sensing Image Target Recognition Based on Deep Convolution Neural Network." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 05 (2019): 2054015. http://dx.doi.org/10.1142/s0218001420540154.

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Target recognition is an important application in the time of high-resolution remote sensing images. However, the traditional target recognition method has the characteristics of artificial design, and the generalization ability is not strong, which makes it difficult to meet the requirement of the current mass data. Therefore, it is urgent to explore new methods for feature extraction and target recognition and location in remote sensing images. Convolutional neural network in deep learning can extract representative and discriminative multi-level features of typical features from images, so

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Xia, Gui-Song, Jin Huang, Nan Xue, Qikai Lu, and Xiaoxiang Zhu. "GeoSay: A geometric saliency for extracting buildings in remote sensing images." Computer Vision and Image Understanding 186 (September 2019): 37–47. http://dx.doi.org/10.1016/j.cviu.2019.06.001.

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Li, Lingling, Pujiang Liang, Jingjing Ma, et al. "A Multiscale Self-Adaptive Attention Network for Remote Sensing Scene Classification." Remote Sensing 12, no. 14 (2020): 2209. http://dx.doi.org/10.3390/rs12142209.

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High-resolution optical remote sensing image classification is an important research direction in the field of computer vision. It is difficult to extract the rich semantic information from remote sensing images with many objects. In this paper, a multiscale self-adaptive attention network (MSAA-Net) is proposed for the optical remote sensing image classification, which includes multiscale feature extraction, adaptive information fusion, and classification. In the first part, two parallel convolution blocks with different receptive fields are adopted to capture multiscale features. Then, the s

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Hu, Gen-sheng, Wen-li Zhou, Dong Liang, and Wen-xia Bao. "Ground Object Information Recovery for Thin Cloud Contaminated Optical Remote Sensing Images." Pattern Recognition and Image Analysis 29, no. 1 (2019): 120–30. http://dx.doi.org/10.1134/s1054661819010127.

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Shabbir, Amsa, Nouman Ali, Jameel Ahmed, et al. "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.

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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

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Pham, Minh-Tan, Luc Courtrai, Chloé Friguet, Sébastien Lefèvre, and Alexandre Baussard. "YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images." Remote Sensing 12, no. 15 (2020): 2501. http://dx.doi.org/10.3390/rs12152501.

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Object detection from aerial and satellite remote sensing images has been an active research topic over the past decade. Thanks to the increase in computational resources and data availability, deep learning-based object detection methods have achieved numerous successes in computer vision, and more recently in remote sensing. However, the ability of current detectors to deal with (very) small objects still remains limited. In particular, the fast detection of small objects from a large observed scene is still an open question. In this work, we address this challenge and introduce an enhanced

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Pires de Lima, Rafael, and Kurt Marfurt. "Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis." Remote Sensing 12, no. 1 (2019): 86. http://dx.doi.org/10.3390/rs12010086.

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Remote-sensing image scene classification can provide significant value, ranging from forest fire monitoring to land-use and land-cover classification. Beginning with the first aerial photographs of the early 20th century to the satellite imagery of today, the amount of remote-sensing data has increased geometrically with a higher resolution. The need to analyze these modern digital data motivated research to accelerate remote-sensing image classification. Fortunately, great advances have been made by the computer vision community to classify natural images or photographs taken with an ordinar

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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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Li, Ming, Deren Li, Bingxuan Guo, Lin Li, Teng Wu, and Weilong Zhang. "Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts." ISPRS International Journal of Geo-Information 7, no. 9 (2018): 361. http://dx.doi.org/10.3390/ijgi7090361.

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Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method based on graph cuts for unmanned aerial vehicle (UAV) remote sensing image mosaicking. This paper first uses a mature and accurate image matching method to register the pre-mosaicked UAV images, and then it marks the source of each pixel in the overlapped area of adjacent images and calculates the energy value contribut

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Wu, Jingqian, and Shibiao Xu. "From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images." Remote Sensing 13, no. 13 (2021): 2620. http://dx.doi.org/10.3390/rs13132620.

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Accurate object detection is important in computer vision. However, detecting small objects in low-resolution images remains a challenging and elusive problem, primarily because these objects are constructed of less visual information and cannot be easily distinguished from similar background regions. To resolve this problem, we propose a Hierarchical Small Object Detection Network in low-resolution remote sensing images, named HSOD-Net. We develop a point-to-region detection paradigm by first performing a key-point prediction to obtain position hypotheses, then only later super-resolving the

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Arun, P. V., K. M. Buddhiraju, A. Porwal, and J. Chanussot. "CNN based spectral super-resolution of remote sensing images." Signal Processing 169 (April 2020): 107394. http://dx.doi.org/10.1016/j.sigpro.2019.107394.

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Ferraris, Vinicius, Nicolas Dobigeon, Yanna Cavalcanti, Thomas Oberlin, and Marie Chabert. "Coupled dictionary learning for unsupervised change detection between multimodal remote sensing images." Computer Vision and Image Understanding 189 (December 2019): 102817. http://dx.doi.org/10.1016/j.cviu.2019.102817.

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Zhou, Dengji, Guizhou Wang, Guojin He, et al. "Robust Building Extraction for High Spatial Resolution Remote Sensing Images with Self-Attention Network." Sensors 20, no. 24 (2020): 7241. http://dx.doi.org/10.3390/s20247241.

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Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is simple, because it contains only two parts: one is the backbone of the network, which is used to learn the local features (short distance context information around the pixel) of buildings from the image; the other part is the pyramid self-attention module, which is used to obtain the global features (l

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Zhai, Y., and D. Ji. "SINGLE IMAGE DEHAZING FOR VISIBILITY IMPROVEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1/W4 (August 27, 2015): 355–60. http://dx.doi.org/10.5194/isprsarchives-xl-1-w4-355-2015.

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Images captured in foggy weather conditions often suffer from poor visibility, which will create a lot of impacts on the outdoor computer vision systems, such as video surveillance, intelligent transportation assistance system, remote sensing space cameras and so on. In this paper, we propose a new transmission estimated method to improve the visibility of single input image (with fog or haze), as well as the image’s details. Our approach stems from two important statistical observations about haze-free images and the haze itself. First, the famous dark channel prior, a statistics of the haze-

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Marsocci, Valerio, Simone Scardapane, and Nikos Komodakis. "MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing." Remote Sensing 13, no. 16 (2021): 3275. http://dx.doi.org/10.3390/rs13163275.

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Scene understanding of satellite and aerial images is a pivotal task in various remote sensing (RS) practices, such as land cover and urban development monitoring. In recent years, neural networks have become a de-facto standard in many of these applications. However, semantic segmentation still remains a challenging task. With respect to other computer vision (CV) areas, in RS large labeled datasets are not very often available, due to their large cost and to the required manpower. On the other hand, self-supervised learning (SSL) is earning more and more interest in CV, reaching state-of-the

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Kiani, Abbas, and Mahmod Reza Sahebi. "Edge detection based on the Shannon Entropy by piecewise thresholding on remote sensing images." IET Computer Vision 9, no. 5 (2015): 758–68. http://dx.doi.org/10.1049/iet-cvi.2013.0192.

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Singh, Dilbag, and Vijay Kumar. "Dehazing of remote sensing images using fourth‐order partial differential equations based trilateral filter." IET Computer Vision 12, no. 2 (2017): 208–19. http://dx.doi.org/10.1049/iet-cvi.2017.0044.

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Lakhal, Mohamed Ilyes, Hakan Çevikalp, Sergio Escalera, and Ferda Ofli. "Recurrent neural networks for remote sensing image classification." IET Computer Vision 12, no. 7 (2018): 1040–45. http://dx.doi.org/10.1049/iet-cvi.2017.0420.

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Liu, Penghua, Xiaoping Liu, Mengxi Liu, et al. "Building Footprint Extraction from High-Resolution Images via Spatial Residual Inception Convolutional Neural Network." Remote Sensing 11, no. 7 (2019): 830. http://dx.doi.org/10.3390/rs11070830.

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The rapid development in deep learning and computer vision has introduced new opportunities and paradigms for building extraction from remote sensing images. In this paper, we propose a novel fully convolutional network (FCN), in which a spatial residual inception (SRI) module is proposed to capture and aggregate multi-scale contexts for semantic understanding by successively fusing multi-level features. The proposed SRI-Net is capable of accurately detecting large buildings that might be easily omitted while retaining global morphological characteristics and local details. On the other hand,

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Botteghi, N., B. Sirmacek, and C. Ünsalan. "RLSNAKE: A HYBRID REINFORCEMENT LEARNING APPROACH FOR ROAD DETECTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 39–45. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-39-2021.

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Abstract. Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study,

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Kaur, Sumit. "Deep Learning Based High-Resolution Remote Sensing Image classification." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 10 (2017): 22. http://dx.doi.org/10.23956/ijarcsse.v7i10.384.

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Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for

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Abdullah, Taghreed, Yakoub Bazi, Mohamad M. Al Rahhal, Mohamed L. Mekhalfi, Lalitha Rangarajan, and Mansour Zuair. "TextRS: Deep Bidirectional Triplet Network for Matching Text to Remote Sensing Images." Remote Sensing 12, no. 3 (2020): 405. http://dx.doi.org/10.3390/rs12030405.

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Exploring the relevance between images and their respective natural language descriptions, due to its paramount importance, is regarded as the next frontier in the general computer vision literature. Thus, recently several works have attempted to map visual attributes onto their corresponding textual tenor with certain success. However, this line of research has not been widespread in the remote sensing community. On this point, our contribution is three-pronged. First, we construct a new dataset for text-image matching tasks, termed TextRS, by collecting images from four well-known different

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Mahdianpari, Masoud, Bahram Salehi, Mohammad Rezaee, Fariba Mohammadimanesh, and Yun Zhang. "Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery." Remote Sensing 10, no. 7 (2018): 1119. http://dx.doi.org/10.3390/rs10071119.

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Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes

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Zhu, Lei, and Li Ma. "Class centroid alignment based domain adaptation for classification of remote sensing images." Pattern Recognition Letters 83 (November 2016): 124–32. http://dx.doi.org/10.1016/j.patrec.2015.12.015.

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Huang, Jian, Shanhui Liu, Yutian Tang, and Xiushan Zhang. "Object-Level Remote Sensing Image Augmentation Using U-Net-Based Generative Adversarial Networks." Wireless Communications and Mobile Computing 2021 (September 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/1230279.

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With the continuous development of deep learning in computer vision, semantic segmentation technology is constantly employed for processing remote sensing images. For instance, it is a key technology to automatically mark important objects such as ships or port land from port area remote sensing images. However, the existing supervised semantic segmentation model based on deep learning requires a large number of training samples. Otherwise, it will not be able to correctly learn the characteristics of the target objects, which results in the poor performance or even failure of semantic segment

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Illarionova, Svetlana, Dmitrii Shadrin, Alexey Trekin, Vladimir Ignatiev, and Ivan Oseledets. "Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks." Sensors 21, no. 16 (2021): 5646. http://dx.doi.org/10.3390/s21165646.

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The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make it possible to generate artificial spectral information, for example, to solve the image colorization problem. In this research, we aim to investigate whether this approach can produce not only visually similar images but also an artificial spectral

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Muhammad, Usman, Weiqiang Wang, Abdenour Hadid, and Shahbaz Pervez. "Bag of words KAZE (BoWK) with two‐step classification for high‐resolution remote sensing images." IET Computer Vision 13, no. 4 (2019): 395–403. http://dx.doi.org/10.1049/iet-cvi.2018.5069.

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Achasov, Andrii, Alla Achasova, Ganna Titenko, Oleg Seliverstov, and Vladimir Krivtsov. "Assessment of the Ecological Condition of Soil Cover Based on Remote Sensing Data: Erosional Aspect." SHS Web of Conferences 100 (2021): 05014. http://dx.doi.org/10.1051/shsconf/202110005014.

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Soil erosion by water is the most important global environmental problem. A modern system for assessing and monitoring soil erosional degradation should be based on the use of remote sensing data. This raises the issue of correct data decoding. The article proposes a method for visual interpretation of eroded soils according to the Sentinel image obtained in the visible range. The authors give some combinations of decoding signs to determine the manifestations of linear and surface water erosion from images. The article shows possible errors in decoding the manifestations of water erosion and

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Pan, Xin, Jian Zhao, and Jun Xu. "An End-to-End and Localized Post-Processing Method for Correcting High-Resolution Remote Sensing Classification Result Images." Remote Sensing 12, no. 5 (2020): 852. http://dx.doi.org/10.3390/rs12050852.

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Since the result images obtained by deep semantic segmentation neural networks are usually not perfect, especially at object borders, the conditional random field (CRF) method is frequently utilized in the result post-processing stage to obtain the corrected classification result image. The CRF method has achieved many successes in the field of computer vision, but when it is applied to remote sensing images, overcorrection phenomena may occur. This paper proposes an end-to-end and localized post-processing method (ELP) to correct the result images of high-resolution remote sensing image class

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Zhang, Jun, Tingjin Luo, Gui Gao, and Lin Lian. "Junction Point Detection Algorithm for SAR Image." International Journal of Antennas and Propagation 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/357379.

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In this paper, we propose a novel junction point detector based on an azimuth consensus for remote sensing images. To eliminate the impact of noise and some noncorrelated edges of SAR image, an azimuth consensus constraint is developed. In addition to detecting the locations of junctions at the subpixel level, this operator recognizes their structures as well. A new formula that includes a minimization criterion for the total weighted distance is proposed to compute the locations of junction points accurately. Compared with other well-known detectors, including Forstner, JUDOCA, and CPDA, the

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