Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

Corey F. Bangs; Fred A. Kruse; Chris R. Olsen

doi:10.1117/12.2015201

18 May 2013 Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

Corey F. Bangs, Fred A. Kruse, Chris R. Olsen

Author Affiliations +

Proceedings Volume 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX; 87431X (2013) https://doi.org/10.1117/12.2015201
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were used from the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) portions of the electromagnetic spectrum. Haralick texture features (contrast, entropy, and correlation) were extracted from the average gray-level image for each of the four spectral ranges studied. A maximum likelihood classifier was trained using a set of ground truth regions of interest (ROIs) and applied separately to the spectral data, texture data, and a fused dataset containing both. Classification accuracy was measured by comparison of results to a separate verification set of test ROIs. Analysis indicates that the spectral range (source of the gray-level image) used to extract the texture feature data has a significant effect on the classification accuracy. This result applies to texture-only classifications as well as the classification of integrated spectral data and texture feature data sets. Overall classification improvement for the integrated data sets was near 1%. Individual improvement for integrated spectral and texture classification of the “Urban” class showed approximately 9% accuracy increase over spectral-only classification. Texture-only classification accuracy was highest for the “Dirt Path” class at approximately 92% for the spectral range from 947 to 1343nm. This research demonstrates the effectiveness of texture feature data for more accurate analysis of hyperspectral data and the importance of selecting the correct spectral range to be used for the gray-level image source to extract these features.

Citation Download Citation

Corey F. Bangs, Fred A. Kruse, and Chris R. Olsen "Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement", Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431X (18 May 2013); https://doi.org/10.1117/12.2015201

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