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

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. Hyper -spectral 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 ap-proach 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, ob-tained 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 miti-gate 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 ex-traction 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 sug-gested approach is competitive with the state-of-the-art.