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

ACE-OT: Polarimetric SAR data based amplitude contrast enhancement algorithm for offset tracking applications

The use of polarimetric synthetic aperture radar (SAR) data can improve the performance of persistent scatterer interferometry (PSI). However, its huge potential remains locked for the amplitude information-based offset tracking (OT) technology. For example, to the best knowledge of the authors, there is no single example of a polarization-based image optimization method that has been developed for OT processing. In this article, an amplitude contrast enhancement (ACE) algorithm is introduced, which demonstrates the potential of the polarimetric SAR data on the improvement of OT performance. Its core idea is finding the optimal combination of the different scattering mechanisms for each pixel to improve the contrast. First, the orientation of the reflected polarization ellipse is removed, to avoid the influence of the geometric relationship between the antenna and the target, and the properties of the target. Then three similarity parameters are defined to represent the three basic reflection types of the single bounce, the double bounce, and the random reflection. After that, the optimizing equation is constructed with two optimizing vectors. Finally, the optimizing vectors are calculated to obtain the enhanced amplitude image. Three examples of the enhancement are presented with different PolSAR images sets of both full- (Radarsat-2) and dual-polarization (TerraSAR-X and Sentinel-1). The performance of ACE-OT has been compared with another method, the adaptive histogram enhancement (AHE). The impact of the number of polarization channels available on ACE-OT performance has also been studied.This work was supported in part by the China Scholarship Council under Grant 201806420035, in part by the Spanish Ministry of Science and Innovation (MCIN), in part by the State Research Agency (AEI) Project under Grant PID2020-117303GB-C21 and Grant MCIN/AEI/10.13039/501100011033, in part by the National Natural Science Foundation of China under Grant 42004011, in part by the China Postdoctoral Science Foundation under Grant 2020M671646, and in part by the Construction Program of Space-Air-Ground-Well Cooperative Awareness Spatial Information Project under Grant B20046.Peer ReviewedPostprint (author's final draft

Improved POLSAR Image Classification by the Use of Multi-Feature Combination

Polarimetric SAR (POLSAR) provides a rich set of information about objects on land surfaces. However, not all information works on land surface classification. This study proposes a new, integrated algorithm for optimal urban classification using POLSAR data. Both polarimetric decomposition and time-frequency (TF) decomposition were used to mine the hidden information of objects in POLSAR data, which was then applied in the C5.0 decision tree algorithm for optimal feature selection and classification. Using a NASA/JPL AIRSAR POLSAR scene as an example, the overall accuracy and kappa coefficient of the proposed method reached 91.17% and 0.90 in the L-band, much higher than those achieved by the commonly applied Wishart supervised classification that were 45.65% and 0.41. Meantime, the overall accuracy of the proposed method performed well in both C- and P-bands. Polarimetric decomposition and TF decomposition all proved useful in the process. TF information played a great role in delineation between urban/built-up areas and vegetation. Three polarimetric features (entropy, Shannon entropy, T11 Coherency Matrix element) and one TF feature (HH intensity of coherence) were found most helpful in urban areas classification. This study indicates that the integrated use of polarimetric decomposition and TF decomposition of POLSAR data may provide improved feature extraction in heterogeneous urban areas

Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints

This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints. We construct this model by jointly modeling the pixels in a patch (rather than an individual pixel) so as to effectively capture the local correlation in the PolSAR images. More importantly, a responsibility parameter is introduced to the proposed model, providing not only the possibility to represent the importance of different pixels within a patch but also the additional flexibility for incorporating the spatial information. As such, double constraints are further imposed by simultaneously utilizing the similarities of the neighboring pixels, respectively, defined on two different parameter spaces (i.e., the hyperparameter in the posterior distribution of mixing coefficients and the responsibility parameter). Furthermore, the variational inference algorithm is developed to achieve effective learning of the proposed SVWMM with the closed-form updates, facilitating the automatic determination of the cluster number. Experimental results on several PolSAR data sets from both airborne and spaceborne sensors demonstrate that the proposed method is effective and it enables better performances on unsupervised classification than the conventional methods