Polarimetric SAR Change Detection with the Complex Hotelling-Lawley Trace Statistic

Accepted manuscript version. Published version at http://dx.doi.org/10.1109/TGRS.2016.2532320.In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace statistic for measuring the similarity of two covariance matrices. The distribution of the Hotelling-Lawley trace statistic is ap- proximated by a Fisher-Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detections rates and error rates that are comparable with the generalized likelihood ratio test

Visualization of and Software for Omnibus Test Based Change Detected in a Time Series of Polarimetric SAR Data

Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution and a factorization of this test statistic with associated p-values, change analysis in a time series of multilook polarimetric synthetic aperture radar data in the covariance matrix representation is carried out. The omnibus test statistic and its factorization detect if and when change occurs. Using airborne EMISAR and spaceborne RADARSAT-2 data, this article focuses on change detection based on the p-values, on visualization of change at pixel as well as segment level, and on computer software

On the Ability of PolSAR Measurements to Discriminate Among Mangrove Species

In this article, a polarimetric synthetic aperture radar (PolSAR) feature is analyzed to discriminate among different mangrove species. This feature, which is related to the Wishart distance, maximizes the contrast among mangrove species optimizing the ratio between quadratic forms. The discrimination performance is assessed both against ground truth and by intercomparing it with conventional model-based decomposition features. Results obtained by processing actual $L$ - and $C$ -band full-polarimetric synthetic aperture radar scenes collected by ALOS-PALSAR-2 and RADARSAT-2 missions show that the proposed approach achieves accurate enough discrimination performance to differentiate two out of the four mangrove species. In addition, results suggest using a multifrequency PolSAR approach to maximize discrimination performance

Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels affect the learning of the translation function. We propose two new network architectures trained with loss functions weighted by priors that reduce the impact of change pixels on the learning objective. The change prior is derived in an unsupervised fashion from relational pixel information captured by domain-specific affinity matrices. Specifically, we use the vertex degrees associated with an absolute affinity difference matrix and demonstrate their utility in combination with cycle consistency and adversarial training. The proposed neural networks are compared with the state-of-the-art algorithms. Experiments conducted on three real data sets show the effectiveness of our methodology

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

Monitoring marine plastic pollution using radar: from source to sea

Marine plastic pollution poses a significant threat to ocean ecosystems worldwide, necessitating effective monitoring and management strategies. The use of remote sensing plays a vital role in providing large-scale, frequently-timed data for monitoring this issue. A multi-modal system has been deemed the most appropriate for tackling the monitoring of marine debris and pollution. Synthetic Aperture Radar (SAR) can provide a wealth of data by taking advantage of the systems ability to acquire in near all-weather conditions, night and daytime. However, research in radar and SARs capability in monitoring marine plastic pollution is lacking. This thesis aims to provide an insight into these capabilities. This is through a series of experiments and investigations into the responses of SAR / Radar to marine plastic litter. Chapter two presents a real-world scenario of plastic accumulation within a river environment. The use of SAR imagery is employed to identify plastic accumulations in two separate study locations. A hypothesis of SAR backscattering interactions with plastic debris is presented. A suite of detectors are subsequently implemented to understand how to best utilise the SAR signal for marine debris detection in these test cases, with the best detector used to create heatmaps of debris accumulation within our test sites. The following chapter provides the results of two rigorous measurement campaigns, where C- and X-band radar data are exploited in a lab experiment. Backscatter and statistical analysis are undertaken across multiple tests involving differing plastic items, concentrations, and wave conditions. From this, interactions between plastic size, shape, and wave conditions are explored. A new interaction for backscatter interactions with plastic debris is also presented. The final data chapter investigates the potential use of a proxy for plastic pollution. Two measurement campaigns are conducted which utilise plastisphere based surfactants, and their interactions for wave dampening, to understand if this is detectable in radar data. For the first time, detailed analysis of backscatter values from differing plastic items and concentrations are presented, as well as the utilisation of real-world test cases. The results obtained in this thesis provide novel insights and additions to recent literature that contributes to our understanding of the capabilities of radar for marine plastic pollution monitoring, as well as new information that can be used in the planning for future missions and studies on the remote sensing of marine plastic pollution