Spectral Segmentation Multi-Scale Feature Extraction Residual Networks for Hyperspectral Image Classification

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (17), p.4219
• Main Authors: Wang, Jiamei, Ren, Jiansi, Peng, Yinbin, Shi, Meilin
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Artificial neural networks; Band spectra; Classification; Deep learning; Feature extraction; grouped convolution; hyperspectral image (HSI); Hyperspectral imaging; Image classification; Image processing; Image segmentation; Information processing; Information retrieval; Machine learning; Modules; multi-scale; Neural networks; Receptive field; Remote sensing; residual structure; Spatial data; Spectral bands; spectral segmentation; Support vector machines
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15174219

Hyperspectral image (HSI) classification is a vital task in hyperspectral image processing and applications. Convolutional neural networks (CNN) are becoming an effective approach for categorizing hyperspectral remote sensing images as deep learning technology advances. However, traditional CNN usually uses a fixed kernel size, which limits the model’s capacity to acquire new features and affects the classification accuracy. Based on this, we developed a spectral segmentation-based multi-scale spatial feature extraction residual network (MFERN) for hyperspectral image classification. MFERN divides the input data into many non-overlapping sub-bands by spectral bands, extracts features in parallel using the multi-scale spatial feature extraction module MSFE, and adds global branches on top of this to obtain global information of the full spectral band of the image. Finally, the extracted features are fused and sent into the classifier. Our MSFE module has multiple branches with increasing ranges of the receptive field (RF), enabling multi-scale spatial information extraction at both fine- and coarse-grained levels. On the Indian Pines (IP), Salinas (SA), and Pavia University (PU) HSI datasets, we conducted extensive experiments. The experimental results show that our model has the best performance and robustness, and our proposed MFERN significantly outperforms other models in terms of classification accuracy, even with a small amount of training data.