A Framework for Benchmarking of Feature Extraction Methods in Earth Observation image Analysis

Abstract

The multitude of sensors used to acquire Earth Observation (EO) images have led to the creation of an extremely various collection of data. Along with appropriate methods able to work with great amount of data, informat ion retrieval process requires algorithms to cope with a range of input imagery. Even if the geometry and the manner of creating Synthetic Aperture Radar (SAR) images are totally different than multispectral data, there are attempts of finding a common ground such that optical image indexing algorithms can be applied for SAR data and vice versa. Moreover, new concepts must be defined in order to obtain satisfying results, enabling measurements and comparisons between the extracted features [4]. Regarding this idea, the goal is to develop an application capable to join feature extraction algorithms and classification algorithms . Its success will sustain the integration of a reliable EO data search engine. This paper presents a framework for feature extraction and classification aiming to support EO image annotation. Weber Local Descriptors (WLD), Gabor filter and Support Vector Machine (SVM) are combined in order to define an application to be tested on both SAR and optical data.