Automated High-resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge

Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery

Classification of Compact Polarimetric Synthetic Aperture Radar Images

The RADARSAT Constellation Mission (RCM) was launched in June 2019. RCM, in addition to dual-polarization (DP) and fully quad-polarimetric (QP) imaging modes, provides compact polarimetric (CP) mode data. A CP synthetic aperture radar (SAR) is a coherent DP system in which a single circular polarization is transmitted followed by the reception in two orthogonal linear polarizations. A CP SAR fully characterizes the backscattered field using the Stokes parameters, or equivalently, the complex coherence matrix. This is the main advantage of a CP SAR over the traditional (non-coherent) DP SAR. Therefore, designing scene segmentation and classification methods using CP complex coherence matrix data is advocated in this thesis. Scene classification of remotely captured images is an important task in monitoring the Earth's surface. The high-resolution RCM CP SAR data can be used for land cover classification as well as sea-ice mapping. Mapping sea ice formed in ocean bodies is important for ship navigation and climate change modeling. The Canadian Ice Service (CIS) has expert ice analysts who manually generate sea-ice maps of Arctic areas on a daily basis. An automated sea-ice mapping process that can provide detailed yet reliable maps of ice types and water is desirable for CIS. In addition to linear DP SAR data in ScanSAR mode (500km), RCM wide-swath CP data (350km) can also be used in operational sea-ice mapping of the vast expanses in the Arctic areas. The smaller swath coverage of QP SAR data (50km) is the reason why the use of QP SAR data is limited for sea-ice mapping. This thesis involves the design and development of CP classification methods that consist of two steps: an unsupervised segmentation of CP data to identify homogeneous regions (superpixels) and a labeling step where a ground truth label is assigned to each super-pixel. An unsupervised segmentation algorithm is developed based on the existing Iterative Region Growing using Semantics (IRGS) for CP data and is called CP-IRGS. The constituents of feature model and spatial context model energy terms in CP-IRGS are developed based on the statistical properties of CP complex coherence matrix data. The superpixels generated by CP-IRGS are then used in a graph-based labeling method that incorporates the global spatial correlation among super-pixels in CP data. The classifications of sea-ice and land cover types using test scenes indicate that (a) CP scenes provide improved sea-ice classification than the linear DP scenes, (b) CP-IRGS performs more accurate segmentation than that using only CP channel intensity images, and (c) using global spatial information (provided by a graph-based labeling approach) provides an improvement in classification accuracy values over methods that do not exploit global spatial correlation

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

The Polarimetric Detection Optimization Filter and Its Statistical Test for Ship Detection

Ship detection via synthetic aperture radar (SAR) has been demonstrated to be very useful as polarimetric information helps discriminate between targets and sea clutter. Among the available polarimetric detectors, optimal polarimetric detection (OPD) theoretically provides the best detection performance under the assumption that the fully developed speckle hypothesis stands. This study proposes a polarimetric detection optimization filter (PDOF). The target clutter ratio (TCR) over the speckle variation was maximized using a matrix transform to derive the PDOF. The objective function based on a matrix transform instead of a vector transform is optimized to obtain synthetic effects by combining a polarimetric whitening filter (PWF) and a polarimetric matched filter (PMF). Subspace form of the PDOF (SPDOF) is also proposed, which gives performance comparable to the PDOF. Assuming a Wishart distribution, the exact and approximate expressions of the closed-form probability density function (PDF) of the PDOF are derived. The probability of false alarm (PFA) was derived in a closed-form expression, which allows obtaining the PDOF threshold analytically. Moreover, the gamma model is extended to a generalized gamma distribution (GΓD) to adapt complicated resolutions and sea states. Experiments with simulated and real data validate the correctness and effectiveness of the results. The PDOF detector achieves the best performance in most virtual and real-world environments, especially in cases where the target statistics and clutter are not Wishart-distributed

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

PolSAR Ship Detection Based on Neighborhood Polarimetric Covariance Matrix

The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms

Optimum graph cuts for pruning binary partition trees of polarimetric SAR images

This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed.Peer ReviewedPostprint (author's final draft

Sentinel-1 InSAR coherence for land cover mapping: a comparison of multiple feature-based classifiers

This article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain—interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.Peer ReviewedPostprint (published version