Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. 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 be aware of, if not at the leading edge of, of advancements like DL. 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 it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional 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.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Theory and measure of certain image norms in SAR

The principal properties of synthetic aperture radar SAR imagery of point and distributed objects are summarized. Against this background, the response of a SAR (Synthetic Aperture Radar) to the moving surface of the sea is considered. Certain conclusions are drawn as to the mechanism of interaction between microwaves and the sea surface. Focus and speckle spectral tests may be used on selected SAR imagery for areas of the ocean. The fine structure of the sea imagery is sensitive to processor focus and adjustment. The ocean reflectivity mechanism must include point like scatterers of sufficient radar cross section to dominate the return from certain individual resolution elements. Both specular and diffuse scattering mechanisms are observed together, to varying degree. The effect is sea state dependent. Several experiments are proposed based on imaging theory that could assist in the investigation of reflectivity mechanisms

Automatic refocus and feature extraction of single-look complex SAR signatures of vessels

In recent years, spaceborne synthetic aperture radar ( SAR) technology has been considered as a complement to cooperative vessel surveillance systems thanks to its imaging capabilities. In this paper, a processing chain is presented to explore the potential of using basic stripmap single-look complex ( SLC) SAR images of vessels for the automatic extraction of their dimensions and heading. Local autofocus is applied to the vessels' SAR signatures to compensate blurring artefacts in the azimuth direction, improving both their image quality and their estimated dimensions. For the heading, the orientation ambiguities of the vessels' SAR signatures are solved using the direction of their ground-range velocity from the analysis of their Doppler spectra. Preliminary results are provided using five images of vessels from SLC RADARSAT-2 stripmap images. These results have shown good agreement with their respective ground-truth data from Automatic Identification System ( AIS) records at the time of the acquisitions.Postprint (published version

Change detection in SAR time-series based on the coefficient of variation

This paper discusses change detection in SAR time-series. Firstly, several statistical properties of the coefficient of variation highlight its pertinence for change detection. Then several criteria are proposed. The coefficient of variation is suggested to detect any kind of change. Then other criteria based on ratios of coefficients of variations are proposed to detect long events such as construction test sites, or point-event such as vehicles. These detection methods are evaluated first on theoretical statistical simulations to determine the scenarios where they can deliver the best results. Then detection performance is assessed on real data for different types of scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative evaluation is performed with a comparison of our solutions with state-of-the-art methods