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JS
2016

Deep Learning for Remote Sensing Image Understanding

7 years 11 months ago
Deep Learning for Remote Sensing Image Understanding
abstract concepts on top of less abstract ones. These highlevel feature representations are more powerful and robust in typical visual tasks. In the intelligent interpretation of remote sensing images, the automatic target detection (or recognition) and highresolution remotely sensed image classification are two hot topics, and both of these two tasks are carried out by first computing the low-level features in the raw images. For different kinds of remote sensing images (e.g., SAR images and hyperspectral images), the corresponding specific feature representations are available. Through applying deep learning methods, we are free of these handcrafted low-level features and can automatically learn mid-level and higher-level ones from a large amount of unlabeled raw samples beyond the types and domains of remote sensing images. Deep leaning methods can undoubtedly offer better feature representations for the related remote sensing task, and there is a bright prospect of seeing more and ...
Liangpei Zhang, Gui-Song Xia, Tianfu Wu, Liang Lin
Added 07 Apr 2016
Updated 07 Apr 2016
Type Journal
Year 2016
Where JS
Authors Liangpei Zhang, Gui-Song Xia, Tianfu Wu, Liang Lin, Xue-Cheng Tai
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