Hyperspectral image analysis by scale-orientation morphological profiles

Antonio J. Plaza; Pablo Martinez; Rosa M. Perez; Javier Plaza

doi:10.1117/12.511110

5 February 2004 Hyperspectral image analysis by scale-orientation morphological profiles

Antonio J. Plaza, Pablo Martinez, Rosa M. Perez, Javier Plaza

Proceedings Volume 5238, Image and Signal Processing for Remote Sensing IX; (2004) https://doi.org/10.1117/12.511110
Event: Remote Sensing, 2003, Barcelona, Spain

Abstract

Mathematical morphology is a classic nonlinear image processing technique that has been successfully applied to analysis and classification of remotely sensed grayscale image data. The extension of basic morphological operations (i.e. erosion and dilation) to multi/hyperspectral imagery is not straightforward. In our approach, we treat the data at each pixel as a vector and impose a partial ordering of vectors in the selected vector space, based on their spectral purity. As a result, basic morphological operations are defined by extension, allowing joint spatial/spectral analysis of remotely sensed multispectral data. In this paper, we introduce the concept of scale-orientation morphological profile, and explore its application to mixed-pixel analysis and classification of hyperspectral data. The effectiveness of the proposed approach is assessed by using both simulated and real hyperspectral datasets collected by the NASA/Jet Propulsion Laboratory Airborne Visible-Infrared Imaging Spectrometer (AVIRIS). The proposed method is successfully applied for the purpose of land-cover classification and delineation of agricultural fields located at the Salinas Valley in California.

Citation Download Citation

Antonio J. Plaza, Pablo Martinez, Rosa M. Perez, and Javier Plaza "Hyperspectral image analysis by scale-orientation morphological profiles", Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); https://doi.org/10.1117/12.511110

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