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

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

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

Multitemporal SAR and polarimetric SAR optimization and classification: Reinterpreting temporal coherence

In multitemporal synthetic aperture radar (SAR) and polarimetric SAR (PolSAR), coherence is a capital parameter to exploit common information between temporal acquisitions. Yet, its use is limited to high coherences. This article proposes the analysis of low-coherence scenarios by introducing a reinterpretation of coherence. It is demonstrated that coherence results from the product of two terms accounting for coherent and radiometric changes, respectively. For low coherences, the first term presents low values, preventing its exploitation for information retrieval. The information provided by the second term can be used in these circumstances to exploit common information. This second term is proposed, as an alternative to coherence, for information retrieval for low coherences. Besides, it is shown that polarimetry allows the temporal optimization of its values. To prove the benefits of this approach, multitemporal SAR and PolSAR data classification is considered as a tool, showing that improvements of the classification overall accuracy may range between 20% and 50%, compared to classification based on coherence.This work was supported in part by the National Natural Science Foundation of China under Grant 61871413, in part by the China Scholarship Council under Grant 2020006880033, and in part by the Project INTERACT funded by the Spanish MCIN/AEI/10.13039/501100011033 under Grant PID2020-114623RB-C32.Peer ReviewedPostprint (author's final draft

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

Wavelet Operators and Multiplicative Observation Models - Application to Change-Enhanced Regularization of SAR Image Time Series

This paper first provides statistical properties of wavelet operators when the observation model can be seen as the product of a deterministic piecewise regular function (signal) and a stationary random field (noise). This multiplicative observation model is analyzed in two standard frameworks by considering either (1) a direct wavelet transform of the model or (2) a log-transform of the model prior to wavelet decomposition. The paper shows that, in Framework (1), wavelet coefficients of the time series are affected by intricate correlation structures which affect the signal singularities. Framework (2) is shown to be associated with a multiplicative (or geometric) wavelet transform and the multiplicative interactions between wavelets and the model highlight both sparsity of signal changes near singularities (dominant coefficients) and decorrelation of speckle wavelet coefficients. The paper then derives that, for time series of synthetic aperture radar data, geometric wavelets represent a more intuitive and relevant framework for the analysis of smooth earth fields observed in the presence of speckle. From this analysis, the paper proposes a fast-and-concise geometric wavelet based method for joint change detection and regularization of synthetic aperture radar image time series. In this method, geometric wavelet details are first computed with respect to the temporal axis in order to derive generalized-ratio change-images from the time series. The changes are then enhanced and speckle is attenuated by using spatial bloc sigmoid shrinkage. Finally, a regularized time series is reconstructed from the sigmoid shrunken change-images. An application of this method highlights the relevancy of the method for change detection and regularization of SENTINEL-1A dual-polarimetric image time series over Chamonix-Mont-Blanc test site