Scene Classification of Remote Sensing Image Based on Multi-scale Feature and Deep Neural Network

doi:10.11947/j.AGCS.2016.20150623

Abstract

Abstract: Aiming at low precision of remote sensing image scene classification owing to small sample sizes, a new classification approach is proposed based on multi-scale deep convolutional neural network (MS-DCNN), which is composed of nonsubsampled Contourlet transform (NSCT), deep convolutional neural network (DCNN), and multiple-kernel support vector machine (MKSVM). Firstly, remote sensing image multi-scale decomposition is conducted via NSCT. Secondly, the decomposing high frequency and low frequency subbands are trained by DCNN to obtain image features in different scales. Finally, MKSVM is adopted to integrate multi-scale image features and implement remote sensing image scene classification. The experiment results in the standard image classification data sets indicate that the proposed approach obtains great classification effect due to combining the recognition superiority to different scenes of low frequency and high frequency subbands.

Key words: remote sensing image, scene classification, deep convolutional neural network, nonsubsampled Contourlet transform, multiple-kernel support vector machine

CLC Number:

P237

XU Suhui, MU Xiaodong, ZHAO Peng, MA Ji. Scene Classification of Remote Sensing Image Based on Multi-scale Feature and Deep Neural Network[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(7): 834-840.

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