Deep learning ensemble method for classification of satellite hyperspectral images

Abstract

Classification of hyperspectral image(HSI) is extensively utilized for the study of remotely sensed satellite images for various real-life applications. Convolutional Neural Networks (CNNs) are a commonly used deep learning technique for image data processing. The utilization of 2D CNNs and 3D CNNs have gained popularity in recent years for classification of hyperspectral image, and a lot of architectures with the combination of two have been proposed some of which include residual network based architectures. So, far individual models have been proposed and ensembling has not been explored much. In this paper, we propose an inception inspired architecture (IIA) and ensembled it with existing architectures HybridSN and inception residual network. The proposed IIA has 3D and 2D inception blocks which enable better spectral-spatial learning features. The Experiments are conducted on three well known publicly available HSI datasets and the results are compared to the state-of-the-art techniques. Experimental results yield that proposed deep learning ensemble method provides enhanced performance as compared to the state-of-the-art techniques. The python source code of the proposed method is available at https://github.com/shyamfec/DL-Ensemble-Method. © 2021 Elsevier B.V.