Feature Extraction for Patch-Based Classification of Multispectral Earth Observation Images

Recently, various patch-based approaches have emerged for high and very high resolution multispectral image classification and indexing. This comes as a consequence of the most important particularity of multispectral data: objects are represented using several spectral bands that equally influence the classification process. In this letter, by using a patch-based approach, we are aiming at extracting descriptors that capture both spectral information and structural information. Using both the raw texture data and the high spectral resolution provided by the latest sensors, we propose enhanced image descriptors based on Gabor, spectral histograms, spectral indices, and bag-of-words framework. This approach leads to a scene classification that outperforms the results obtained when employing the initial image features. Experimental results on a WorldView-2 scene and also on a test collection of tiles created using Sentinel 2 data are presented. A detailed assessment of speed and precision was provided in comparison with state-of-the-art techniques. The broad applicability is guaranteed as the performances obtained for the two selected data sets are comparable, facilitating the exploration of previous and newly lunched satellite missions.