A High Resolution SAR Image Spectral Analysis Framework for Multiple Class Image Mining in Urban Areas

Traditional approaches for Synthetic Aperture Radar image mining aim at finding accurate representations and models for specific categories of targets. Usually the task of achieving classifications with large number of classes is left for high resolution multispectral images or polarimetric SAR images, although for the latter the number of discoverable classes is significantly lower. This paper discusses the opportunity to use high-resolution SAR image spectra to obtain an increase in information content that can be extracted from the data, in order to identify a large number of classes directly from SLC images. The discussion features three spectrum processing algorithms, incorporating an information theory method, an approach for spectrum description and a spectrum estimation method. The results show that high-resolution SAR images can be used to obtain a statistical description of high accuracy for urban areas

Multi Temporal Analysis of Floods and Tsunami Effects: Annotation and Quantitative Analysis

This paper addresses the problem of multitemporal analysis of an available TerraSAR-X data time series covering the Sendai region in order to assess flood extent and damages caused by Tohuku-oki tsunami. Over the last decade the use of Earth Observation satellites to support disaster and emergency relief has considerably grown. In order to fully exploit highresolution satellite images, a method based on patches (each image is divided into non-overlapping tiles) is proposed to extract relevant contextual information. The local features of each patch act as a compact content descriptor. Further on, considering the available descriptors, the next step is to cluster the data in order to find similar semantic classes. The SVM classifier implements the concept of query by example using image content. The results include well-defined semantic classes, derived through semiautomatic methods thus developing an effective approach to the multitemporal analysis

Mutual Information Based Measure for Image Content Characterization

In this paper, we use primitive feature extraction and clustering to code the image information content. Our purpose is to describe the complexity of the information based on the combinational profile of the clustered primitive features using entropic measures like mutual information and Kullback-Leibler divergence

Earth Observation Images Information Mining for Flooding and Security Related Applications

Image Information Mining lends itself well to security related applications

Knowledge based image information mining from earth observation images

The content of earth observation images can be analyzed by knowledge-based image information mining

Multi-Modal Change Detection based on Information Theoretical Similarity Measures

The discovery of changes in image time series can be based on information theoretical similarity measures

Contextual Descriptors for Scene Classes in Very High Resolution SAR Images

This paper proposes a feature extraction method for image patches in order to capture the spatial context. The method is based on the characteristics of the spectra of the SAR data, integrating radiometric, geometric, and texture properties of the SAR image patch