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Recently, convolutional neural networks (CNNs) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution (SR) from multitemporal unregistered imagery have received little attention so far. This article proposes a novel CNN-based technique that exploits both spatial and temporal correlations to combine multiple images. This novel framework integrates the spatial registration task directly inside the CNN, and allows one to exploit the representation learning capabilities of the network to enhance registration accuracy. The entire SR process relies on a single CNN with three main stages: shared 2-D convolutions to extract high-dimensional features from the input images; a subnetwork proposing registration filters derived from the high-dimensional feature representations; 3-D convolutions for slow fusion of the features from multiple images. The whole network can be trained end-to-end to recover a single high-resolution image from multiple unregistered low-resolution images. The method presented in this article is the winner of the PROBA-V SR challenge issued by the European Space Agency (ESA). [ABSTRACT FROM AUTHOR]

Compressive sensing promises to enable bandwidth-efficient on-board compression of astronomical data by lifting the encoding complexity from the source to the receiver. The signal is recovered off-line, exploiting graphical processing unit (GPU)’s parallel computation capabilities to speedup the reconstruction process. However, inherent GPU hardware constraints limit the size of the recoverable signal and the speedup practically achievable. In this work, we design parallel algorithms that exploit the properties of circulant matrices for efficient GPU-accelerated sparse signals recovery. Our approach reduces the memory requirements, allowing us to recover very large signals with limited memory. In addition, it achieves a 10-fold signal recovery speedup, thanks to ad-hoc parallelization of matrix–vector multiplications and matrix inversions. Finally, we practically demonstrate our algorithms in a typical application of circulant matrices: deblurring a sparse astronomical image in the compressed domain. [ABSTRACT FROM PUBLISHER]

Hyperspectral images exhibit significant spectral correlation, whose exploitation is crucial for compression. In this paper, we investigate the problem of predicting a given band of a hyperspectral image using more than one previous band. We present an information-theoretic analysis based on the concept of conditional entropy, which is used to assess the available amount of correlation and the potential compression gain. Then, we propose a new lossless compression algorithm that employs a Kalman filter in the prediction stage. Simulation results are presented on Airborne Visible Infrared Imaging Spectrometer, Hyperspectral Digital Imagery Collection Experiment, and Hyperspectral Mapper scenes, showing competitive performance with other state-of-the-art compression algorithms. [ABSTRACT FROM AUTHOR]

Transform-based lossy compression has a huge potential for hyperspectral data reduction. Hyperspectral data are 3-D, and the nature of their correlation is different in each dimension. This calls for a careful design of the 3-D transform to be used for compression. In this paper, we investigate the transform design and rate allocation stage for lossy compression of hyperspectral data. First, we select a set of 3-D transforms, obtained by combining in various ways wavelets, wavelet packets, the discrete cosine transform, and the Karhunen-Loève transform (KLT), and evaluate the coding efficiency of these combinations. Second, we propose a low-complexity version of the KLT, in which complexity and performance can be balanced in a scalable way, allowing one to design the transform that better matches a specific application. Third, we integrate this, as well as other existing transforms, in the framework of Part 2 of the Joint Photographic Experts Group (JPEG) 2000 standard, taking advantage of the high coding efficiency of JPEG 2000, and exploiting the interoperability of an international standard. We introduce an evaluation framework based on both reconstruction fidelity and impact on image exploitation, and evaluate the proposed algorithm by applying this framework to AVIRIS scenes. It is shown that the scheme based on the proposed low-complexity KLT significantly outperforms previous schemes as to rate-distortion performance. As for impact on exploitation, we consider multiclass hard classification, spectral unmixing, binary classification, and anomaly detection as benchmark applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. We show that, due to the statistical properties of the SAR signal, an along-range linear predictor with few taps is able to effectively capture most of the raw signal correlation. The proposed predictive coding algorithm, which performs quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off, with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods based on transform-coding; fractional output bit-rates can also be achieved in the entropy-constrained mode. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems. [ABSTRACT FROM AUTHOR]

A preface to the "International Journal of Remote Sensing" is presented.