Zhuo Zheng, Yanfei Zhong, Ailong Ma, Liangpei Zhang
Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping patches. As such, these methods are local learning methods, which have a high computational cost. In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification. In FPGA, an encoder-decoder based FCN is utilized to consider the global spatial information by processing the whole image, which results in fast inference. However, it is difficult to directly utilize the encoder-decoder based FCN for HSI classification as it always fails to converge due to the insufficiently diverse gradients caused by the limited training samples. To solve the divergence problem and maintain the abilities of FCN of fast inference and global spatial information mining, a global stochastic stratified sampling strategy is first proposed by transforming all the training samples into a stochastic sequence of stratified samples. This strategy can obtain diverse gradients to guarantee the convergence of the FCN in the FPGA framework. For a better design of FCN architecture, FreeNet, which is a fully end-to-end network for HSI classification, is proposed to maximize the exploitation of the global spatial information and boost the performance via a spectral attention based encoder and a lightweight decoder. A lateral connection module is also designed to connect the encoder and decoder, fusing the spatial details in the encoder and the semantic features in the decoder. The experimental results obtained using three public benchmark datasets suggest that the FPGA framework is superior to the patch-based framework in both speed and accuracy for HSI classification. Code has been made available at: https://github.com/Z-Zheng/FreeNet.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Hyperspectral | Pavia University | AA@200 | 99.83 | FPGA |
| Hyperspectral | Pavia University | Kappa@200 | 0.9974 | FPGA |
| Hyperspectral | Pavia University | OA@200 | 99.81 | FPGA |
| Hyperspectral | CASI University of Houston | Average Accuracy | 88.44 | FPGA |
| Hyperspectral | CASI University of Houston | Kappa | 0.8555 | FPGA |
| Hyperspectral | CASI University of Houston | Overall Accuracy | 86.61 | FPGA |
| Hyperspectral | Salinas | AA@200 | 99.91 | FPGA |
| Hyperspectral | Salinas | Kappa@200 | 0.9991 | FPGA |
| Hyperspectral | Salinas | OA@200 | 99.92 | FPGA |
| Image Classification | Pavia University | AA@200 | 99.83 | FPGA |
| Image Classification | Pavia University | Kappa@200 | 0.9974 | FPGA |
| Image Classification | Pavia University | OA@200 | 99.81 | FPGA |
| Image Classification | CASI University of Houston | Average Accuracy | 88.44 | FPGA |
| Image Classification | CASI University of Houston | Kappa | 0.8555 | FPGA |
| Image Classification | CASI University of Houston | Overall Accuracy | 86.61 | FPGA |
| Image Classification | Salinas | AA@200 | 99.91 | FPGA |
| Image Classification | Salinas | Kappa@200 | 0.9991 | FPGA |
| Image Classification | Salinas | OA@200 | 99.92 | FPGA |
| Hyperspectral Image Segmentation | Pavia University | AA@200 | 99.83 | FPGA |
| Hyperspectral Image Segmentation | Pavia University | Kappa@200 | 0.9974 | FPGA |
| Hyperspectral Image Segmentation | Pavia University | OA@200 | 99.81 | FPGA |
| Hyperspectral Image Segmentation | CASI University of Houston | Average Accuracy | 88.44 | FPGA |
| Hyperspectral Image Segmentation | CASI University of Houston | Kappa | 0.8555 | FPGA |
| Hyperspectral Image Segmentation | CASI University of Houston | Overall Accuracy | 86.61 | FPGA |
| Hyperspectral Image Segmentation | Salinas | AA@200 | 99.91 | FPGA |
| Hyperspectral Image Segmentation | Salinas | Kappa@200 | 0.9991 | FPGA |
| Hyperspectral Image Segmentation | Salinas | OA@200 | 99.92 | FPGA |