Scene semantic classification based on random-scale stretched convolutional neural network for high-spatial resolution remote sensing imagery

Yanfei Liu, Yanfei Zhong, Feng Fei, Liangpei Zhang

Abstract: Convolutional neural network (CNN) has outstanding performance on nature image classification, such as facial recognition, ImageNet Large Scale Visual Recognition Challenge. However, due to scale variation of the same object in scene, it's difficult to directly utilize CNN for remote sensing image classification. In order to solve this problem, scene classification based on a random-scale stretched convolutional neural network (SRSCNN) for HSR remote sensing imagery is proposed in this paper. In the proposed method, the patches with random scale is cropped from image and stretched to the specified scale as input to train CNN, and in order to further improve the performance of CNN, the proposed method classifies an image multiple times to decide its label by voting. Experimental results using two datasets, i.e. the UC Merced dataset, Google Dataset of SIRI-WHU, show better performance than the traditional scene classification methods.