Performance of Scattering Matrix Decomposition and Color Spaces for Synthetic Aperture Radar Imagery

Polarimetrc Synthetic Aperture Radar (SAR) has been shown to be a powerful tool in remote sensing because uses up to four simultaneous measurements giving additional degrees of freedom for processing. Typically, polarization decomposition techniques are applied to the polarization-dependent data to form colorful imagery that is easy for operators systems to interpret. Yet, the presumption is that the SAR system operates with maximum bandwidth which requires extensive processing for near- or real-time application. In this research, color space selection is investigated when processing sparse polarimetric SAR data as in the case of the publicly available \Gotcha Volumetric SAR Data Set, Version 1:0 . To improve information quality in resultant color imagery, three scattering matrix decompositions were investigated (linear, Pauli and Krogager) using two common color spaces (RGB, CMY) to determine the best combination for accurate feature extraction. A mathematical model is presented for each decomposition technique and color space to the Cramer-Rao lower bound (CRLB) and quantify the performance bounds from an estimation perspective for given SAR system and processing parameters. After a deep literature review in color science, the mathematical model for color spaces was not able to be computed together with the mathematical model for decomposition techniques. The color spaces used for this research were functions of variables that are out of the scope of electrical engineering research and include factors such as the way humans sense color, environment influences in the color stimulus and device technical characteristics used to display the SAR image. Hence, SAR imagery was computed for specific combinations of decomposition technique and color space and allow the reader to gain an abstract view of the performance differences

Detecting covariance symmetries for classification of polarimetric SAR images

The availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection and/or recognition capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, coherence matrix, Muller matrix, and to exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric SAR images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image

Analysis of Polarimetric Synthetic Aperture Radar and Passive Visible Light Polarimetric Imaging Data Fusion for Remote Sensing Applications

The recent launch of spaceborne (TerraSAR-X, RADARSAT-2, ALOS-PALSAR, RISAT) and airborne (SIRC, AIRSAR, UAVSAR, PISAR) polarimetric radar sensors, with capability of imaging through day and night in almost all weather conditions, has made polarimetric synthetic aperture radar (PolSAR) image interpretation and analysis an active area of research. PolSAR image classification is sensitive to object orientation and scattering properties. In recent years, significant work has been done in many areas including agriculture, forestry, oceanography, geology, terrain analysis. Visible light passive polarimetric imaging has also emerged as a powerful tool in remote sensing for enhanced information extraction. The intensity image provides information on materials in the scene while polarization measurements capture surface features, roughness, and shading, often uncorrelated with the intensity image. Advantages of visible light polarimetric imaging include high dynamic range of polarimetric signatures and being comparatively straightforward to build and calibrate. This research is about characterization and analysis of the basic scattering mechanisms for information fusion between PolSAR and passive visible light polarimetric imaging. Relationships between these two modes of imaging are established using laboratory measurements and image simulations using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. A novel low cost laboratory based S-band (2.4GHz) PolSAR instrument is developed that is capable of capturing 4 channel fully polarimetric SAR image data. Simple radar targets are formed and system calibration is performed in terms of radar cross-section. Experimental measurements are done using combination of the PolSAR instrument with visible light polarimetric imager for scenes capturing basic scattering mechanisms for phenomenology studies. The three major scattering mechanisms studied in this research include single, double and multiple bounce. Single bounce occurs from flat surfaces like lakes, rivers, bare soil, and oceans. Double bounce can be observed from two adjacent surfaces where one horizontal flat surface is near a vertical surface such as buildings and other vertical structures. Randomly oriented scatters in homogeneous media produce a multiple bounce scattering effect which occurs in forest canopies and vegetated areas. Relationships between Pauli color components from PolSAR and Degree of Linear Polarization (DOLP) from passive visible light polarimetric imaging are established using real measurements. Results show higher values of the red channel in Pauli color image (|HH-VV|) correspond to high DOLP from double bounce effect. A novel information fusion technique is applied to combine information from the two modes. In this research, it is demonstrated that the Degree of Linear Polarization (DOLP) from passive visible light polarimetric imaging can be used for separation of the classes in terms of scattering mechanisms from the PolSAR data. The separation of these three classes in terms of the scattering mechanisms has its application in the area of land cover classification and anomaly detection. The fusion of information from these particular two modes of imaging, i.e. PolSAR and passive visible light polarimetric imaging, is a largely unexplored area in remote sensing and the main challenge in this research is to identify areas and scenarios where information fusion between the two modes is advantageous for separation of the classes in terms of scattering mechanisms relative to separation achieved with only PolSAR

Application of Polarimetric GPR to detection of subsurface objects

Tohoku University佐藤源之課

General model-based decomposition framework for polarimetric SAR images

2017 Spring.Includes bibliographical references.Polarimetric synthetic aperture radars emit a signal and measure the magnitude, phase, and polarization of the return. Polarimetric decompositions are used to extract physically meaningful attributes of the scatterers. Of these, model-based decompositions intend to model the measured data with canonical scatter-types. Many advances have been made to this field of model-based decomposition and this work is surveyed by the first portion of this dissertation. A general model-based decomposition framework (GMBDF) is established that can decompose polarimetric data with different scatter-types and evaluate how well those scatter-types model the data by comparing a residual term. The GMBDF solves for all the scatter-type parameters simultaneously that are within a given decomposition by minimizing the residual term. A decomposition with a lower residual term contains better scatter-type models for the given data. An example is worked through that compares two decompositions with different surface scatter-type models. As an application of the polarimetric decomposition analysis, a novel terrain classification algorithm of polSAR images is proposed. In the algorithm, the results of state-of-the-art polarimetric decompositions are processed for an image. Pixels are then selected to represent different terrain classes. Distributions of the parameters of these selected pixels are determined for each class. Each pixel in the image is given a score according to how well its parameters fit the parameter distributions of each class. Based on this score, the pixel is either assigned to a predefined terrain class or labeled unclassified

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

Coastal Aquaculture Extraction Using GF-3 Fully Polarimetric SAR Imagery: A Framework Integrating UNet++ with Marker-Controlled Watershed Segmentation

Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries