Scene classification of high-resolution remote sensing image based on deep convolution neural network

doi:10.13474/j.cnki.11-2246.2019.0211

Abstract

Abstract: Scene classification makes great sense to the understanding and information extraction of high-resolution remote sensing images. The traditional method has used low-level, middle-level or semantic features to distinguish the class of the image scene, but the low or middle level features can't exactly describe the image which are more detailed and complex. In this paper, a DCNN scene classification model based on deep convolution neural network is proposed. The convolution layer is used to extract the image texture, color and other low-level features firstly. Then we use the pool layer to select important features. Finally, the extracted features are merged into high-level semantic features which are used to classify the high resolution remote sensing images. To solve the problem of over fitting, data augmentation, regularization and Dropout are used to improve the generalization ability. This method has obtained 91.33% accuracies on UC Merced-21. Compared with traditional method, the classification accuracies is effectively improved. At the same time, the superiority of deep convolution neural network in remote sensing image classification is proved.

Key words: high-resolution remote sensing image, scene classification, DCNN, overfitting, feature combination

CLC Number:

P237

MENG Qingxiang, WU Xuan. Scene classification of high-resolution remote sensing image based on deep convolution neural network[J]. 测绘通报, 2019, 0(7): 17-22.

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