Spectral unmixing of multispectral satellite images with dimensionality expansion using morphological profiles

Sergio Bernabé; Prashanth Reddy Marpu; Antonio Plaza; Jon Atli Benediktsson

doi:10.1117/12.930418

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19 October 2012 Spectral unmixing of multispectral satellite images with dimensionality expansion using morphological profiles

Sergio Bernabé, Prashanth Reddy Marpu, Antonio Plaza, Jon Atli Benediktsson

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Proceedings Volume 8514, Satellite Data Compression, Communications, and Processing VIII; 85140Z (2012) https://doi.org/10.1117/12.930418
Event: SPIE Optical Engineering + Applications, 2012, San Diego, California, United States

Abstract

In this paper, we develop a new framework for spectral unmixing of multispectral remote sensing images with limited spectral resolution. Our proposed approach performs dimensionality expansion by taking advantage of the spatial information contained in such images. For this purpose, in this work, we experiment with morphological profiles and morphological attribute filters, which allow expanding the dimensionality of the original image and obtaining a detailed signature (profile) at each pixel using the SVM classifier. This allows for the application of spectral unmixing techniques that integrate both the spatial and the spectral information, since the unmixing is not only based on the original multispectral/color information but also takes into account the additional bands included by exploiting the spatial information. The unmixing chain considered in this work comprises a classic endmember extraction algorithm: vertex component analysis (VCA) followed by fully constrained linear spectral unmixing (FCLSU) to estimate the abundance of each endmember in each pixel of the image. Kernel principal component analysis (KPCA) is also used in the considered chain, to increase dimensionality in the spectral domain only and to perform feature extraction. In order to quantitatively validate the proposed framework, we use the RGB bands of a set of registered hyperspectral images. Specifically, we use the ground-truth to validate the unmixing results obtained for the lower spatial resolution scenes. Our experimental results indicate that the proposed dimensionality expansion strategy allows for the successful unmixing of multispectral satellite images, specially for RGB/color images.

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Sergio Bernabé, Prashanth Reddy Marpu, Antonio Plaza, and Jon Atli Benediktsson "Spectral unmixing of multispectral satellite images with dimensionality expansion using morphological profiles", Proc. SPIE 8514, Satellite Data Compression, Communications, and Processing VIII, 85140Z (19 October 2012); https://doi.org/10.1117/12.930418

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