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

Analysis of Min-Trees over Sentinel-1 Time Series for Flood Detection

International audienceMonitoring flood is an important task for disaster management. It requires to distinguish between changes related to water from the other changes. We address such an issue by relying on both spatial and intensity information. To do so, we exploit min-tree that emphasize intensity extrema in a multiscale, efficient framework. We thus suggest a two-step approach operating on satellite image time series. We first perform a temporal analysis to identify images containing possible floods. Then a spatial analysis is achieved to detect flood areas on the selected images. Both steps relies on the analysis of component attributes extracted from the min-tree representation. We conduct some experiments on a flooded scene observed through Sentinel-1 SAR imagery. The results show that flood areas can be efficiently and accurately characterized with spatial component attributes extracted from hierarchical representations from SAR time series

Statistical modeling of polarimetric SAR data: a survey and challenges

Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate statistical models for PolSAR data, and a number of distributions have been proposed. In order to see the differences of various models and to make a comparison among them, a survey is provided in this paper. Texture models, which could capture the non-Gaussian behavior observed in high resolution data, and yet keep a compact mathematical form, are mainly explained. Probability density functions for the single look data and the multilook data are reviewed, as well as the advantages and applicable context of those models. As a summary, challenges in the area of statistical analysis of PolSAR data are also discussed.Peer ReviewedPostprint (published version

Signal Models for Changes in Polarimetric SAR Data

Synthetic aperture radar (SAR) polarimetry can improve change detection in terms of detection capabilities. In this work, we are proposing to extend the idea of target decomposition to changes affecting partial targets. This will allow the separation of polarimetric-dependent changes, providing extra information that can be used to better understand the processes affecting the targets. Three models for changes are proposed and compared. The methodologies are based on Lagrangian optimizations of distinct operators built using quadratic forms for a power ratio and a power difference. The optimizations can be accomplished by diagonalizations of specific matrices derived from polarimetric covariance matrices. These are, therefore, spectral decompositions of an appropriate matrix which we define as change matrix. The theoretical validity of the models is assessed using Monte Carlo simulations. Additionally, we perform real data validation exploiting L-band quad-polarimetric data from the E-SAR (DLR) SARTOM 2006 campaign and ALOS PALSAR (JAXA) acquisitions in Morecombe Bay (U.K.). We observed that the two algorithms based on power difference allow to decompose the change into the minimal set of scattering mechanisms (SMs) that have been added or removed from the scene. The two algorithms differ on the initial assumption on the change. On the other hand, the ratio operator provides a better detection performance although the eigenvalues do not correspond to meaningful SMs. A combination of the three methodologies can, therefore, improve detection and classification of changes

Introducing artificial data generation in active learning for land use/land cover classification

Fonseca, J., Douzas, G., & Bacao, F. (2021). Increasing the effectiveness of active learning: Introducing artificial data generation in active learning for land use/land cover classification. Remote Sensing, 13(13), 1-20. [2619]. https://doi.org/10.3390/rs13132619In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both expensive and time consuming to implement. Most of the current literature focuses on the optimization of AL by modifying the selection criteria and the classifiers used. Although improvements in these areas will result in more effective data collection, the use of artificial data sources to reduce human–computer interaction remains unexplored. In this paper, we introduce a new component to the typical AL framework, the data generator, a source of artificial data to reduce the amount of user-labeled data required in AL. The implementation of the proposed AL framework is done using Geometric SMOTE as the data generator. We compare the new AL framework to the original one using similar acquisition functions and classifiers over three AL-specific performance metrics in seven benchmark datasets. We show that this modification of the AL framework significantly reduces cost and time requirements for a successful AL implementation in all of the datasets used in the experiment.publishersversionpublishe

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports