Extraction of spatial features in hyperspectral images based on the analysis of differential attribute profiles

Nicola Falco; Jon Atli Benediktsson; Lorenzo Bruzzone

doi:10.1117/12.2029199

17 October 2013 Extraction of spatial features in hyperspectral images based on the analysis of differential attribute profiles

Nicola Falco, Jon Atli Benediktsson, Lorenzo Bruzzone

Author Affiliations +

Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 88920O (2013) https://doi.org/10.1117/12.2029199
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

The new generation of hyperspectral sensors can provide images with a high spectral and spatial resolution. Recent improvements in mathematical morphology have developed new techniques such as the Attribute Profiles (APs) and the Extended Attribute Profiles (EAPs) that can effectively model the spatial information in remote sensing images. The main drawbacks of these techniques is the selection of the optimal range of values related to the family of criteria adopted to each filter step, and the high dimensionality of the profiles, which results in a very large number of features and therefore provoking the Hughes phenomenon. In this work, we focus on addressing the dimensionality issue, which leads to an highly intrinsic information redundancy, proposing a novel strategy for extracting spatial information from hyperspectral images based on the analysis of the Differential Attribute Profiles (DAPs). A DAP is generated by computing the derivative of the AP; it shows at each level the residual between two adjacent levels of the AP. By analyzing the multilevel behavior of the DAP, it is possible to extract geometrical features corresponding to the structures within the scene at different scales. Our proposed approach consists of two steps: 1) a homogeneity measurement is used to identify the level L in which a given pixel belongs to a region with a physical meaning; 2) the geometrical information of the extracted regions is fused into a single map considering their level L previously identified. The process is repeated for different attributes building a reduced EAP, whose dimensionality is much lower with respect to the original EAP ones. Experiments carried out on the hyperspectral data set of Pavia University area show the effectiveness of the proposed method in extracting spatial features related to the physical structures presented in the scene, achieving higher classification accuracy with respect to the ones reported in the state-of-the-art literature

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Nicola Falco, Jon Atli Benediktsson, and Lorenzo Bruzzone "Extraction of spatial features in hyperspectral images based on the analysis of differential attribute profiles", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88920O (17 October 2013); https://doi.org/10.1117/12.2029199

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