2016/02/11 by Siwei Feng, Feng, Siwei, Yuki Itoh +5
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Geochemistry and Geologic Mapping #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Remote-Sensing Image Classification
paper · pdf · doi:10.48550/arxiv.1602.03903
openalex publication_date 2016/02/11 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene. The classification procedure can be operated directly on hyperspectral data or performed by using some features extracted from the corresponding hyperspectral signatures containing information like the signature's energy or shape. In this paper, we describe a technique that applies non-homogeneous hidden Markov chain (NHMC) models to hyperspectral signature classification. The basic idea is to use statistical models (such as NHMC) to characterize wavelet coefficients which capture the spectrum semantics (i.e., structural information) at multiple levels. Experimental results show that the approach based on NHMC models can outperform existing approaches relevant in classification tasks.