Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

High-spectral resolution imaging provides critical insights into important computer vision tasks such as classification, tracking, and remote sensing. Modern Snapshot Spectral Imaging (SSI)systems directly acquire the entire 3D data-cube through the intelligent combination of spectral filters and detector elements. Partially because of the dramatic reduction in acquisition time, SSI systems exhibit limited spectral resolution, for example, by associating each pixel with a single spectral band in Spectrally Resolvable Detector Arrays. In this paper, we propose a novel machine learning technique aiming to enhance the spectral resolution of imaging systems by exploiting the mathematical framework of Sparse Representations (SR). Our formal approach proposes a systematic way to estimate a high-spectral resolution pixel from a measured low-spectral resolution version by appropriately identifying a sparse representation that can directly generate the highspectral resolution output. We enforce the sparsity constraint by learning a joint space coding dictionary from multiple low and high spectral resolution training data and we demonstrate that one can successfully reconstruct high-spectral resolution images from limited spectral resolution measurements.

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Spectral information obtained by hyperspectral sensors enables better characterization, identification and classification of the objects in a scene of interest. Unfortunately, several factors have to be addressed in the classification of hyperspectral data, including the acquisition process, the high dimensionality of spectral samples, and the limited availability of labeled data. Consequently, it is of great importance to design hyperspectral image classification schemes able to deal with the issues of the curse of dimensionality, and simultaneously produce accurate classification results, even from a limited number of training data. To that end, we propose a novel machine learning technique that addresses the hyperspectral image classification problem by employing the state-of-the-art scheme of Convolutional Neural Networks (CNNs). The formal approach introduced in this work exploits the fact that the spatio-spectral information of an input scene can be encoded via CNNs and combined with multi-class classifiers. We apply the proposed method on novel dataset acquired by a snapshot mosaic spectral camera and demonstrate the potential of the proposed approach for accurate classification.