Exponential Weighted Random Forest for Hyperspectral Image Classification

Abstract

Hyperspectral image (HSI) classification is a challenging task due to the problem of a few labeled samples versus the high dimensional features. Random forest (RF) have been applied to the HSI problem in the past. For RF based HSI classification methods, equal weights/votes have been considered to all decision trees in the past. However, this may not be true always for varying test cases. Hence, this paper proposes an Exponentially Weighted Random Forest (EWRF) method which uses dynamic association with trained trees. Thus, we try to capture the relationship between trained trees and test case in EWRF. The performance of the proposed method has been compared with state-of-the-art methods over two well-known benchmark datasets. The experimental results show that EWRF can provide competitive solutions for hyperspectral image classification.