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

Speckle noise reduction in PolSAR images with binary partition tree

In some remote sensing applications such as PolSAR (Polarimetric Synthetic Aperture Radar), the use of Binary Partition Trees (BPTs) for Speckle Noise filtering schemes is currently gaining interest. In this thesis, a new approach using this representation is investigated: branch filtering. This approach consists in searching for each leaf its ancestors and selecting the one that best represents it, that is, the one that yields the lower error. A potentiality assessment is done to evaluate the margin of improvement that new techniques based on this approach may provide and describe the basic specifications of the algorithms based on it. After that, different new techniques are developed, analysed and compared against the State-of-the-Art. We point out the main strengths and weaknesses of each technique. Our main goal is to understand the behaviour of the filtered data along the BPT branch and interpret how this information can be used in the future for speckle noise reduction in PolSAR images. Finally some conclusions are drawn and some possible future lines of work are exposed and commented.En algunas aplicaciones de teledetección como Polarimetric SAR, el uso de Árboles de Decisión Binarios está ganando interés. En esta tésis se incorpora un nuevo método que usa esta representación: filtraje por ramas. Este método consiste en buscar para cada hoja sus antepasados y seleccionar el mejor nodo como el que de el menor error. Se lleva a cabo un análisis de potencialidad para evaluar el margen de mejora que nuevas técnicas basadas en este método podrían proporcionar y se describen los principios basicos de los algoritmos que se basan en él. Tras esto, se desenvolupan distintas técnicas y se comparan con las del estado del arte. De cada técnica, destacamos sus principales fortalezas y debilidades. Nuestro objetivo principal es entender el comportamiento de los datos filtrados a lo largo de la rama del BPT e interpretar como podemos usar esta información en un futuro para la reducción de ruido especular (speckle) en imágenes PolSAR. Por último, se exponen algunas conclusiones y se presentan y comentan algunas posibles líneas de trabajo futuras.En algunes aplicacions de teledetecció com Polarimetric SAR, l'ús d'Arbres de Particio Binària està guanyant interès. En aquesta tesi, s'investiga un nou mètode que utilitza aquesta representació: filtratge per branques. Aquest mètode consisteix en buscar per cada fulla els seus avantpassats i seleccionar el millor node, és a dir, el que doni un error menor. Es duu a terme un analisi de potencialitat per evaluar el marge de millora que noves tècniques basades en aquest mètode podrien aportar i es descriuen els principis bàsics dels algorismes que s'hi basen. Després, es desenvolupen diverses tècniques i es comparen amb les de l'estat de l'art. Destaquem les principals fortalesses i feblesses de cada tècnica. El nostre principal objectiu és entendre el comportament de les dades filtrades al llarg de la branca del BPT i interpretar com podem utilitzar aquesta informació en un futur per la reducció del soroll especular (speckle) en imatges PolSAR. Per últim s'exposen algunes conclusions i es proposen i comenten possibles noves línies de treball

Multidimensional SAR data representation and processing based on Binary Partition Trees

English: A novel multidimensional SAR data abstraction is presented, based on Binary Partition Trees (BPT). This data abstraction is employed for different applications, as data filtering and segmentation, change detection, etc. The BPT can be contructed from a Polarimetric SAR (PolSAR) image or from a serie of coregistered acquisitions, conforming a tool that enables the systematic exploitation of PolSAR datasets simultaneously in the space and time dimensions.Castellano: na nueva abstracción de datos SAR multidimensionales es presentada, basada en Árboles de Partición Binaria (BPT). Esta abstracción de datos se emplea para distintas aplicaciones, como filtrado, segmentación, detección de cambios, etc. El BPT puede construirse a partir de una imagen SAR polarimétrica o de una serie temporal de imágenes, siendo una herramienta que permite la explotación sistemática de sets de datos PolSAR simultáneamente en espacio y tiempo.Català: Una nova abstracció de dades SAR multidimensionals és presentada, basada en Arbres de Partició Binària (BPT). Aquesta abstracció de dades s'empra per a diferents aplicacions, com filtrat, segmentació, detecció de canvis, etc. El BPT es pot construir a partir d'una imatge SAR polarimètrica o d'una sèrie temporal d'imatges, sent una eina que permet l'explotació sistemàtica de sets de dades PolSAR simultàniament en espai i temps

Spatial Adaptive Speckle Filtering Driven by Temporal Polarimetric Statistics and Its Application to PSI

Persistent scatterer (PS) interferometry (PSI) techniques are designed to measure ground deformations using satellite synthetic aperture radar (SAR) data. They rely on the identification of pixels not severely affected by spatial or temporal decorrelation, which, in general, correspond to pointlike PSs commonly found in urban areas. However, in urban areas, we can find not only PSs but also distributed scatterers (DSs) whose phase information may be exploited for PSI applications. Estimation of DS parameters requires speckle filtering to be applied to the complex SAR data, but conventional speckle filtering approaches tend to mask PS information due to spatial averaging. In the context of single-polarization PSI, adaptive speckle filtering strategies based on the exploitation of amplitude temporal statistics have been proposed, which seek to avoid spatial filtering on nonhomogeneous areas. Given the growing interest on polarimetric PSI techniques, i.e., those using polarimetric diversity to increase performance over conventional single-polarization PSI, in this paper, we propose an adaptive spatial filter driven by polarimetric temporal statistics, rather than single-polarization amplitudes. The proposed approach is able to filter DS while preserving PS information. In addition, a new methodology for the joint processing of PS and DS in the context of PSI is introduced. The technique has been tested for two different urban data sets: 41 dual-polarization TerraSAR-X images of Murcia (Spain) and 31 full-polarization Radarsat-2 images of Barcelona (Spain). Results show an important improvement in terms of number of pixels with valid deformation information, hence denser area coverage.This work was supported in part by the Spanish Ministerio de Economía y Competitividad and in part by the European Union FEDER funds under Project TEC2011-28201-C02-02

Multidimensional and temporal SAR data representation and processing based on binary partition trees

This thesis deals with the processing of different types of multidimensional SAR data for distinct applications. Instead of handling the original pixels of the image, which correspond to very local information and are strongly contaminated by speckle noise, a region-based and multiscale data abstraction is defined, the Binary Partition Tree (BPT). In this representation, each region stands for an homogeneous area of the data, grouping pixels with similar properties and making easier its interpretation and processing. The work presented in this thesis concerns the definition of the BPT structures for Polarimetric SAR (PolSAR) images and also for temporal series of SAR acquisitions. It covers the description of the corresponding data models and the algorithms for BPT construction and its exploitation. Particular attention has been paid to the speckle filtering application. The proposed technique has proven to achieve arbitrarily large regions over homogeneous areas while also preserving the spatial resolution and the small details of the original data. As a consequence, this approach has demonstrated an improvement in the performance of the target response estimation with respect to other speckle filtering techniques. Moreover, due to the flexibility and convenience of this representation, it has been employed for other applications as scene segmentation and classification. The processing of SAR time series has also been addressed, proposing different approaches for dealing with the temporal information of the data, resulting into distinct BPT abstractions. These representations have allowed the development of speckle filtering techniques in the spatial and temporal domains and also the improvement and the definition of additional methods for classification and temporal change detection and characterization

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks

Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning (ML) methods proposed in this domain generally focus on utilizing highly discriminative features to improve the classification performance, but this task is complicated by the well-known "curse of dimensionality" phenomena. Other approaches based on deep Convolutional Neural Networks (CNNs) have certain limitations and drawbacks, such as high computational complexity, an unfeasibly large training set with ground-truth labels, and special hardware requirements. In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach. The proposed approach has three advantages. First, there is no requirement for an extensive feature extraction process. Second, it is computationally efficient due to utilized compact configurations. In particular, the proposed compact and adaptive CNN model is designed to achieve the maximum classification accuracy with minimum training and computational complexity. This is of considerable importance considering the high costs involved in labelling in PolSAR classification. Finally, the proposed approach can perform classification using smaller window sizes than deep CNNs. Experimental evaluations have been performed over the most commonly-used four benchmark PolSAR images: AIRSAR L-Band and RADARSAT-2 C-Band data of San Francisco Bay and Flevoland areas. Accordingly, the best obtained overall accuracies range between 92.33 - 99.39% for these benchmark study sites