Land-cover classification with hyperspectral remote sensing image using CNN and spectral band selection

Abstract

Recent developments in remote sensing technology have led to a significant rise in the demand for precise and reliable algorithms for analyzing hyperspectral remote sensing images. Hyperspectral remote sensing image analysis aims to differentiate various types of landscapes on Earth's surface, which is challenging due to the high dimensionality of the data space and the abundance of spectral bands. To address these challenges, this work proposes a novel 3D CNN-based model for classifying hyperspectral images based on both spectral and spatial features. The proposed approach comprises two stages: data pre-processing and classification. First, the raw hyperspectral image is processed, and the essential spectral bands are selected using HyperPCA to reduce the data's high dimensionality. Second, using fused multi-resolution spectral and spatial features, obtained by a 3D convolutional neural network model the landscapes of the hyperspectral remote sensing image are classified. The proposed model employs concatenation and addition operations to extract comprehensive and selective features, respectively, and uses direct max pooling to mitigate strong activations that reduce feature maps. The proposed approach enhances hyperspectral image classification even with a small number of labeled samples by enabling the automatic extraction of pertinent information while maintaining the spatial and spectral features of the data. The Indian Pines and University of Pavia datasets, two existing hyperspectral images, are used in the experiments. The results demonstrate that, in terms of classification performance, the suggested approach is competitive with the state-of-the-art.