Title: Post classification change detection based on feature-based ensemble classifiers

Authors: D.R. Sowmya; P. Deepa Shenoy; K.R. Venugopal

Addresses: Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru, India ' Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru, India ' Bangalore University, Bengaluru, India

Abstract: Change detection is a challenging task in the field of remote sensing. Mainly, the change map is used for disaster assessment, monitoring deforestation and urban studies. In this paper, we present a novel method for post classification change detection. Google Earth images of 2011 and 2016 of Bangalore East are used for the study. Multiple features such as texture features, morphological features are extracted using grey level co-occurrence matrix (GLCM) and morphological operations respectively. Linear discriminant analysis (LDA) is used to reduce the dimension of the selected features for the training set. The proposed ensemble classifier system (ECS) exploits K-nearest neighbour (KNN), support vector machine (SVM) and maximum likelihood classifier (MLC). The proposed method adopts the subsample kernel-based subtraction technique to find the difference image; this method greatly reduces data complexity compared to per-pixel-based image subtraction method.

Keywords: change detection; dimensionality reduction technique; DRT; ensemble classifier; feature extraction; image difference.

DOI: 10.1504/IJSTDS.2021.116958

International Journal of Spatio-Temporal Data Science, 2021 Vol.1 No.2, pp.149 - 169

Received: 30 Oct 2019
Accepted: 23 Feb 2020
Published online: 10 Aug 2021 *

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