A Multi-Dimensional Deep Siamese Network for Land Cover Change Detection in Bi-Temporal Hyperspectral Imagery

Abstract

In this study, an automatic Change Detection (CD) framework based on a multi-dimensional deep Siamese network was proposed for CD in bi-temporal hyperspectral imagery. The proposed method has two main steps: (1) automatic generation of training samples using the Otsu algorithm and the Dynamic Time Wrapping (DTW) predictor, and (2) binary CD using a multidimensional multi-dimensional Convolution Neural Network (CNN). Two bi-temporal hyperspectral datasets of the Hyperion sensor with a variety of land cover classes were used to evaluate the performance of the proposed method. The results were also compared to reference data and two state-of-the-art hyperspectral change detection (HCD) algorithms. It was observed that the proposed method relatively had higher accuracy and lower False Alarm (FA) rate, where the average Overall Accuracy (OA) and Kappa Coefficient (KC) were more than 96% and 0.90, respectively, and the average FA rate was lower than 5%.