High-resolution remote sensing image classification by combining deep learning with nDSM

doi:10.13474/j.cnki.11-2246.2019.0253

Abstract

Abstract: Although many classification methods for high-resolution remote sensing images have been proposed in recent years, there are still some problems (e.g. misclassification and incompleteness of object boundary) due to high intra-class variance and limitation of spectral information of high-resolution remote sensing images. In this paper, a high resolution remote sensing image classification method is proposed by combining nDSM (normalized digital surface model) data and deep learning framework. Firstly, nDSM data is combined with remote sensing image as an additional band to generate new imagery and produce training samples. Then, the optimized U-Net model is trained on the basis of training samples to obtain the optimal model. Finally, remote sensing images are combined with nDSM data to be the input data, and the trained optimal model is performed to get classification results. Experimental results demonstrate that the proposed method can effectively improve the classification performance in terms of classification accuracy.

Key words: image classification, nDSM, deep learning, U-Net

CLC Number:

P237

XU Huimin, QI Hua, NAN Ke, CHEN Min. High-resolution remote sensing image classification by combining deep learning with nDSM[J]. 测绘通报, 2019, 0(8): 63-67.

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