Exploiting sparsity and dictionary learning to efficiently classify materials in hyperspectral imagery.

摘要

Hyperspectral imaging (HSI) produces spatial images with pixels that, instead of consisting of three colors, consist of hundreds of spectral measurements. The dimensionality of the data collected is extremely high, thus making analysis difficult. Frequently, dimension reduction techniques are incorporated in the HSI signal processing chain as a preprocessing step in order to reduce the dimensionality of the data. This reduction and change of basis can occlude the physics of the system.;This research explores the utility of representing the high-dimensional HSI data in a learned dictionary basis for the express purpose of material identification and classification. Multiclass classification is performed on the transformed data using the RandomForests algorithm. Performance results are reported.;In addition to classification, single material detection is considered also. Commonly used detection algorithm performance is demonstrated on both raw radiance pixels and HSI represented in dictionary-learned bases. Comparison results are shown which indicate that detection on dictionary-learned sparse representations perform as well as detection on radiance. In addition, a different method of performing detection, capitalizing on dictionary learning is established and performance comparisons are reported, showing gains over traditional detection methods.