Spectral-spatial DNA encoding discriminative classifier for hyperspectral remote sensing imagery

Authors:
Publisher website
Google Scholar
Abstract
Hyperspectral remote sensing image classification is one of the most challenging tasks. In our previous work, motivated by the similarity between the structures of DNA and hyperspectral remote sensing images, a DNA matching mechanism was used to transform the hyperspectral remote sensing image into a DNA cube for classification. However, the above DNA encoding strategy lacks the process of encoding accurate spectral and spatial feature into the DNA cube, resulting in unsatisfying classification performance. In this paper, a spectral-spatial DNA encoding strategy for encoding accurate spectral and spatial feature of hyperspectral remote sensing image is proposed. In the spectral dimension, the first-order spectral curve is encoded into the DNA cube, while in the spatial dimension, the principal components or their corresponding texture feature (GLCM) are encoded into the DNA cube. Finally, different with the previous DNA encoding classifier using genetic algorithm (GA), the paper combines the discriminative classifier (i.e. SVM) with spectral-spatial DNA encoding to improve classification performance for hyperspectral remote sensing imagery. The experimental results confirmed the effectiveness of the newly devised DNA encoding strategy and the discriminative classifier in classifying the DNA cube.

This publication has 8 references indexed in Scilit: