Hyperspectral Image Classification Based on the Improved Spectral Former

Hyperspectral imaging has become a powerful tool in remote sensing, enabling fine-grained material identification and revealing the chemical and physical properties of materials. Its applications span urban land-use mapping, object recognition, crop classification, and agricultural yield prediction. The typical hyperspectral image classification workflow includes image loading, correction, noise reduction, feature extraction, classifier selection, training, classification, and result output. Feature extraction plays a critical role, but traditional methods such as SIFT, PCA, and LDA are limited in efficiency and accuracy, especially with large-scale datasets. Deep learning, particularly Convolutional Neural Networks (CNNs), has significantly improved classification performance by extracting hierarchical features from raw data. However, challenges remain in capturing both spectral and spatial information effectively. Transformer models, such as SpectralFormer, have been proposed to address these issues by leveraging attention mechanisms to capture long-range dependencies. Yet, they struggle with preserving spatial structures in hyperspectral images. The integration of Atrous Spatial Pyramid Pooling (ASPP) into SpectralFormer offers a promising solution to this problem, enhancing spatial feature extraction and improving overall classification performance. This paper discusses these advancements and highlights the potential of combining deep learning and spatial feature extraction techniques to address the unique challenges of hyperspectral image classification.