Results 21 to 30 of about 3,858,794 (311)
REMOTE SENSING IMAGE CLASSIFICATION WITH THE SEN12MS DATASET [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021 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
M. Schmitt, M. Schmitt, Y.-L. Wu
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Counting Dense Objects in Remote Sensing Images [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing
Guangshuai Gao +2 more
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Generating Natural Adversarial Remote Sensing Images [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Over the last years, Remote Sensing Images (RSI) analysis have started resorting to using deep neural networks to solve most of the commonly faced problems, such as detection, land cover classification or segmentation. As far as critical decision making can be based upon the results of RSI analysis, it is important to clearly identify and understand ...
Jean-Christophe Burnel +3 more
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Remote Sensing Image Information Quality Evaluation via Node Entropy for Efficient Classification
Remote Sensing, 2022 Combining remote sensing images with deep learning algorithms plays an important role in wide applications. However, it is difficult to have large-scale labeled datasets for remote sensing images because of acquisition conditions and costs.
Jiachen Yang +4 more
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Applied Sciences, 2023 Remote sensing scene classification (RSSC) is a very crucial subtask of remote sensing image understanding. With the rapid development of convolutional neural networks (CNNs) in the field of natural images, great progress has been made in RSSC.
Tao Xu, Zhicheng Zhao, Jun Wu
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Remote Sensing Image Change Detection With Transformers [PDF]
IEEE Transactions on Geoscience and Remote Sensing, 2022 Modern change detection (CD) has achieved remarkable success by the powerful discriminative ability of deep convolutions. However, high-resolution remote sensing CD remains challenging due to the complexity of objects in the scene. Objects with the same semantic concept may show distinct spectral characteristics at different times and spatial locations.
Hao Chen 0045 +2 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Water body extraction from remote sensing images is an important task. Deep learning has become a more popular method for extracting water bodies from remote sensing images.
Mengya Li +5 more
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IEEE Access, 2020 Image super-resolution (SR) technique can improve the spatial resolution of images without upgrading the imaging system. As a result, SR promotes the development of high resolution (HR) remote sensing image applications.
Ning Zhang +4 more
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Target Detection Method for Low-Resolution Remote Sensing Image Based on ESRGAN and ReDet
Photonics, 2021 With the widespread use of remote sensing images, low-resolution target detection in remote sensing images has become a hot research topic in the field of computer vision.
Yuwu Wang, Guobing Sun, Shengwei Guo
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Computational ghost imaging for remote sensing [PDF]
Journal of the Optical Society of America A, 2012 Computational ghost imaging is a structured-illumination active imager coupled with a single-pixel detector that has potential applications in remote sensing. Here we report on an architecture that acquires the two-dimensional spatial Fourier transform of the target object (which can be inverted to obtain a conventional image).
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