Matching hyperspectral absorptions by weighted hamming distance

Abstract

Abstract To analyse and compare hyperspectral signatures, features extraction and matching are two key issues. In this letter, hyperspectral absorption features and the corresponding matching algorithm are discussed. First, an absorption detection method is applied to catch all necessary spectral absorptions with improved reliability. Then, a weighted Hamming distance is proposed to match the binary absorption‐features. Next, an elastic matching scheme is designed to classify the hyperspectral data. Experiments of classification are carried out on six classes of vegetation from the Salinas data‐set. Results show that the proposed method not only increased the overall classification accuracy to 73.13% from back propagation neural network's 71.86% and support vector machine's 73.06%, but also improved the error distributions among different classes.

Keywords

• Image recognition
• Computer vision and image processing techniques
• Neural nets
• Support vector machines