Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1810014(2022)
Hyperspectral Image Classification Based on Multi-Scale Feature Fusion Residual Network
In order to fully extract the spectral-spatial features of hyperspectral image (HSI) and to achieve high-precision ground object classification of HSI, an end-to-end multi-scale feature fusion identity (MFFI) block is proposed. This block combines 3D multi-scale convolution, feature fusion and residual connection. Through this block, multi-scale spectral-spatial joint features of HSI can be extracted. Because of the end-to-end feature of the block, the final MFFI network can be obtained by stacking multiple MFFI blocks. The average overall accuracy of 99.73%, average accuracy of 99.84%, and Kappa coefficient of 0.9971 are obtained on three HSI datasets: Salinas, Indian Pines and University of Pavia. The results show that the proposed MFFI block can effectively extract the spectral-spatial features of different types of ground object datasets and achieve satisfactory classification results.
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Ziqing Deng, Yang Wang, Bing Zhang, Zhao Ding, Lifeng Bian, Chen Yang. Hyperspectral Image Classification Based on Multi-Scale Feature Fusion Residual Network[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1810014
Category: Image Processing
Received: Jun. 15, 2021
Accepted: Aug. 10, 2021
Published Online: Aug. 29, 2022
The Author Email: Yang Chen (eliot.c.yang@163.com)