Weighted Residual Dynamic Ensemble Learning for Hyperspectral Image Classification

Abstract

Recently, collaborative representation classifiers have been extensively studied as an essential method for the hyperspectral image. However, how to comprehensively utilize the classification advantages of multiple collaborative classifiers has not been well investigated. In this article, two new dynamic ensemble learning methods using local weighted residual (LWR-DEL) and double-weighted residual (DWR-DEL) of multicollaborative representation classifiers are proposed. First, the dynamic ensemble learning method based on clustering is utilized to introduce prior knowledge for the collaborative representation classifier. Then, with prior knowledge, the local weights of each classifier for a different region of competence are obtained. To consider the global information of hyperspectral data, the K-nearest neighbor algorithm is adopted to achieve validation samples with global information. The global weights for each classifier can be obtained and then used to constrain the locally weighted residuals. Similar to LWR-DEL, the global information is also used to constrain residual, and then double-weighted constrained residual fusion obtains the final classifier result. The effectiveness of the proposed methods is validated using three hyperspectral data sets. The experimental results show that both LWR-DEL and DWR-DEL outperform their single-classifier counterparts. In particular, the proposed methods provide superior performance compared with the state-of-the-art methods.

Keywords

• Dynamic ensemble learning
• hyperspectral imagery
• multicollaborative representation
• weighted residual ensemble