Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
TL;DR: In this article, the authors provide a comprehensive survey of state-of-the-art remote sensing deep learning research for remote sensing applications, focusing on theories, tools, and challenges for the remote sensing community.
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Abstract: In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.
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References
Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
Michele Volpi,Devis Tuia +1 more
TL;DR: In this article, a downsample-then-upsample architecture is proposed to learn a rough spatial map of high-level representations by means of convolutions and then upsample them back to the original resolution by deconvolutions.
336
•Posted Content
Highway Networks
TL;DR: In this article, the authors introduce highway networks, which allow unimpeded information flow across several layers on information highways and use gating units to regulate the flow of information through a network.
330
Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning
TL;DR: The opportunities provided by manifold learning for classification of remotely sensed data are demonstrated and limitations and opportunities remain both for research and applications.
330
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks
TL;DR: This paper shows how a convolutional neural network can be applied to multispectral orthoimagery and a digital surface model of a small city for a full, fast and accurate per-pixel classification.
324
High-resolution satellite scene classification using a sparse coding based multiple feature combination
TL;DR: The sparse coding method for satellite scene classification is introduced, a two-stage linear support vector machine (SVM) classifier is designed and an improved rotation invariant texture descriptor based on LTPs is presented.
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