Hyperspectral image classification method based on multi-scale proximal feature concatenate network - Hongmin GAO; Xueying CAO; Zhonghao CHEN; Zaijun HUA; Chenming LI; Yue CHEN (Tongxin xuebao, 2021)
Aiming at the phenomenon that the hyperspectral classification algorithm based on traditional CNN model was not expressive enough in detail and the network structure was too complex, a hyperspectral image classification method based on multi-scale proximal feature concatenate network (MPFCN) was designed.By introducing multi-scale filter and cavity convolution, the model could be kept light and the discriminative features of the space spectrum could be obtained, and the correlation between the proximal features of the CNN was proposed to further enhance the detail expression.Experimental results on three benchmark hyperspectral image data sets show that the proposed method is superior to other classification models.