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.

Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community

Hyperspectral images (HSIs) have fine spectral information, and rich spatial information, of which the feature quality is one of the key factors that affect the classification performance. Therefore, how to extract essential features, and eliminate redundant features from hyperspectral data are the main research focus of this article. Here, we propose a spectral-spatial feature extraction method based on ensemble empirical mode decomposition for HSI classification, which contains several steps as follows: First, the dimension reduction for HSI is performed by using the principal component analysis method. Second, in order to decrease the sensitivity to noise, and extract rough outline features, the adaptive total variation filtering (ATVF) is conducted on the selected principal components. Furthermore, by using the ensemble empirical mode decomposition (EEMD) to resolve each spectral band into sequence components, the features of HSIs can be better coalesced into the transform domain. Finally, the first $K$ principal components of the input image, and the outputs of the ATVF, and EEMD are integrated into a stacking system to obtain the final feature image, which is then classified by a pixel-wise classifier. The experimental results of three authentic hyperspectral datasets show that the proposed algorithm obtains a superior classification performance compared with other methods.

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Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

Modern hyperspectral imaging technologies generate enormous datasets that could potentially transmit a wealth of information, but such a resource presents numerous difficulties for data analysis and interpretation. Deep learning techniques undoubtedly provide a wide range of potential for solving both traditional imaging tasks and exciting new problems in the spatial-spectral domain. This is true in the primary application area of remote sensing, where hyperspectral technology originated and has made the majority of its progress, but it may be even more true in the vast array of now existing and developing application areas that make use of these imaging technologies. The current review advances on two fronts: on the one hand, it is directed at domain experts who desire an updated overview of how deep learning architectures might work in conjunction with hyperspectral acquisition techniques to address specific tasks in various application sectors. On the other hand, by providing them with a picture of how deep learning technologies are applied to hyperspectral data from (near)real-time perspective. The contributions of this review include the existence of these two points of view and the inclusion of opportunities and important problems associated with the development of future CHIME mission to be launched by European Space Agency (ESA). 

Deep Learning for On-board Processing of Imaging Spectroscopy: A Survey

Hyperspectral image classification in remote sensing discipline aims to analyze scene properties of the environment captured from earth observing satellites of aircrafts. To comprehend this aim common linear methods like principal component analysis and linear discriminant analysis fail to model the nonlinear structures of data. To learn feature representations on large-scale high-dimensional data, deep learning methods have been applied successfully. We utilize a deep neural network for both feature extraction and then classification based on unsupervised pre-training using stacked denoising autoencoder method and supervised fine-tuning using logistic regression on top. This paper both exploit joint representation, namely spectral-spatial information of hyperspectral images to pre-train classification capturing the most salient features. Besides that, since extracting sparse features might improve the discrimination, rectified linear unit (ReLU) is used as activation function in encoders to extract high-level sparse features. The results show in our experiments that this model achieves the higher classification accuracy than other evaluation methods, and excels classical classifiers namely support vector machines and random forests.

Pre-trained Classification of Hyperspectral Images Using Denoising Autoencoders and Joint Features

This article presents a new hyperspectral image classification method, which is capable of automatic feature learning while achieving high classification accuracy. The method contains the following...

Hyperspectral classification via deep networks and superpixel segmentation

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Hyperspectral classification via deep networks and superpixel segmentation