Advanced techniques for classification of polarimetric synthetic aperture radar data

With various remote sensing technologies to aid Earth Observation, radar-based imaging is one of them gaining major interests due to advances in its imaging techniques in form of syn-thetic aperture radar (SAR) and polarimetry. The majority of radar applications focus on mon-itoring, detecting, and classifying local or global areas of interests to support humans within their efforts of decision-making, analysis, and interpretation of Earth’s environment. This thesis focuses on improving the classification performance and process particularly concerning the application of land use and land cover over polarimetric SAR (PolSAR) data. To achieve this, three contributions are studied related to superior feature description and ad-vanced machine-learning techniques including classifiers, principles, and data exploitation. First, this thesis investigates the application of color features within PolSAR image classi-fication to provide additional discrimination on top of the conventional scattering information and texture features. The color features are extracted over the visual presentation of fully and partially polarimetric SAR data by generation of pseudo color images. Within the experiments, the obtained results demonstrated that with the addition of the considered color features, the achieved classification performances outperformed results with common PolSAR features alone as well as achieved higher classification accuracies compared to the traditional combination of PolSAR and texture features. Second, to address the large-scale learning challenge in PolSAR image classification with the utmost efficiency, this thesis introduces the application of an adaptive and data-driven supervised classification topology called Collective Network of Binary Classifiers, CNBC. This topology incorporates active learning to support human users with the analysis and interpretation of PolSAR data focusing on collections of images, where changes or updates to the existing classifier might be required frequently due to surface, terrain, and object changes as well as certain variations in capturing time and position. Evaluations demonstrated the capabilities of CNBC over an extensive set of experimental results regarding the adaptation and data-driven classification of single as well as collections of PolSAR images. The experimental results verified that the evolutionary classification topology, CNBC, did provide an efficient solution for the problems of scalability and dynamic adaptability allowing both feature space dimensions and the number of terrain classes in PolSAR image collections to vary dynamically. Third, most PolSAR classification problems are undertaken by supervised machine learn-ing, which require manually labeled ground truth data available. To reduce the manual labeling efforts, supervised and unsupervised learning approaches are combined into semi-supervised learning to utilize the huge amount of unlabeled data. The application of semi-supervised learning in this thesis is motivated by ill-posed classification tasks related to the small training size problem. Therefore, this thesis investigates how much ground truth is actually necessary for certain classification problems to achieve satisfactory results in a supervised and semi-supervised learning scenario. To address this, two semi-supervised approaches are proposed by unsupervised extension of the training data and ensemble-based self-training. The evaluations showed that significant speed-ups and improvements in classification performance are achieved. In particular, for a remote sensing application such as PolSAR image classification, it is advantageous to exploit the location-based information from the labeled training data. Each of the developed techniques provides its stand-alone contribution from different viewpoints to improve land use and land cover classification. The introduction of a new fea-ture for better discrimination is independent of the underlying classification algorithms used. The application of the CNBC topology is applicable to various classification problems no matter how the underlying data have been acquired, for example in case of remote sensing data. Moreover, the semi-supervised learning approach tackles the challenge of utilizing the unlabeled data. By combining these techniques for superior feature description and advanced machine-learning techniques exploiting classifier topologies and data, further contributions to polarimetric SAR image classification are made. According to the performance evaluations conducted including visual and numerical assessments, the proposed and investigated tech-niques showed valuable improvements and are able to aid the analysis and interpretation of PolSAR image data. Due to the generic nature of the developed techniques, their applications to other remote sensing data will require only minor adjustments

Mathematical Morphology on the Sphere: Application to Polarimetric Image processing

Projecte final de carrera fet en col.laboració amb Centre de morphologie mathématique, École des Mines de ParisEnglish: The fully polarimetric synthetic aperture radar (PolSAR) provides data containing the complete scattering information. Therefore, these data have drawn more attention in recent years. PolSAR data can be represented as polarization states on a sphere. We present image processing techniques based on the analysis of the polarimetric information within its location on the sphere. Mathematical morphology is a well-known nonlinear approach for image processing. It is based on the computation of minimum and maximum values of local neighborhoods. That necessitates the existence of an ordering relationship between the points to be treated. The lack of a natural ordering on the sphere presents an inherent problem when defining morphological operators extended to unit sphere. We analyze in this project some proposals to the problem of ordering on the unit sphere, leading to formulations of morphological operators suited to the configuration of the data. The notion of local supremum and infimum is introduced, which allows to define the dilation and erosion on the sphere. Supervised orderings are considered and its associated operators for target recognition issues. We also present various filtering procedures for denoising purposes. The diferent methods studied in this project pursuit the generalization of the morphological operators on the sphere. Through the analysis performed, we pretend to achieve an understanding of the data and automation of the target detection.Castellano: El radar de apertura sintética totalmente polarimétrico (PolSAR) proporciona datos que contienen la información completa de dispersión. Estos datos han captado más atención en los últimos años. Los datos PolSAR pueden ser representados como estados de polarización en una esfera. Se presentan las técnicas de procesamiento de imágenes basadas en el análisis de la información polarimétrica y en su ubicación en la esfera. La morfología matemática es una técnica no lineal para el procesamiento de imágenes. Se basa en el cálculo de los valores mínimos y máximos alrededor de un punto. Precisa de la existencia de una relación de orden entre los puntos a tratar. La falta de un orden natural en la esfera presenta un problema inherente a la hora de definir los operadores morfológicos extendidos a la esfera unidad. En este proyecto se analizan algunas propuestas para el problema del orden en la esfera unidad, lo que da lugar a formulaciones de los operadores morfológicos adaptados a la configuración de los datos. Se introduce la noción de supremo e ínfimo local, lo que permite definir la dilatación y la erosión en la esfera. Consideramos órdenes supervisados y sus operadores asociados para problemas de reconocimiento de objetivos. También se presentan varios procedimientos de filtrado para la eliminación de ruido. Los diferentes métodos estudiados en este proyecto persiguen la generalización de los operadores morfológicos a la esfera. A través del análisis realizado, se pretende lograr una comprensión de los datos y la autCatalà: El radar d'obertura sintètica totalment polarimètric (PolSAR) proporciona dades que contenen la informació completa de dispersió. Aquestes dades han captat més atenció en els últims anys. Les dades PolSAR poden ser representades com a estats de polarizació en una esfera. Es presenten tècniques de processament d'imatge basades en l'anàlisi de la informació polarimètrica i en la seva ubicació en l'esfera. La morfologia matemàtica és una tècnica no lineal per al processament d'imatges. Es basa en el càlcul dels valors mínim i màxim al voltant d'un punt. Precisa de l'existència d'una relació d'ordre entre els punts a tractar. La manca d'un ordre natural en l'esfera presenta un problema inherent a l'hora de definir els operadors morfològics estesos a l'esfera unitat. En aquest projecte s'analitzen algunes propostes per al problema de l'ordre en l'esfera unitat, el que dóna lloc a formulacions dels operadors morfològics adaptats a la configuració de les dades. S'introdueix la noció de suprem i mínim local, el que permet definir la dilatació i l'erosió en l'esfera. Considerem ordres supervisats i els seus operadors associats per a problemes de reconeixement d'objectius. També es presenten diversos procediments de filtratge per a la eliminació de soroll. Els diferents mètodes estudiats en aquest projecte busquen la generalizació dels operadors morfològics a l'esfera. Mitjançcant l'anàlisi realitzat, es pretén aconseguir la comprensió de les dades i l'a

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The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

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