A land-cover classification method of high-resolution remote sensing imagery based on convolution neural network

Yuhan Wang; Lingjia Gu; Ruizhi Ren; Xu Zheng; Xintong Fan

doi:10.1117/12.2318930

7 September 2018 A land-cover classification method of high-resolution remote sensing imagery based on convolution neural network

Yuhan Wang, Lingjia Gu, Ruizhi Ren, Xu Zheng, Xintong Fan

Author Affiliations +

Proceedings Volume 10764, Earth Observing Systems XXIII; 107641Y (2018) https://doi.org/10.1117/12.2318930
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

With the development of space satellites, a large number of high-resolution remote sensing images have been produced, so the analysis and application of high-resolution remote sensing images are very important. Recently deep learning provides a new method to increase the accuracy of land-cover classification. This study aims to propose a classification framework based on convolutional neural network (CNN) to carry out remote sensing scene classification. After remote sensing images are trained by CNN, a model which can extract complex characteristic from the image for classification is created. In this paper, GaoFen-2(GF-2) satellite data is used as data sources and Jilin province of China is selected as the study area. Firstly, the preprocessed images are made into a GF-2 satellite data sets. Secondly, CaffeNet is used to train the data sets through Caffe platform and the classification result is obtained. The CNN overall accuracy is 89.88%, the Kappa coefficient is 0.8026. Compared with the traditional BP neural network classification result, it is obviously find the CNN is more suitable for remote sensing image classification.

Citation Download Citation

Yuhan Wang, Lingjia Gu, Ruizhi Ren, Xu Zheng, and Xintong Fan "A land-cover classification method of high-resolution remote sensing imagery based on convolution neural network", Proc. SPIE 10764, Earth Observing Systems XXIII, 107641Y (7 September 2018); https://doi.org/10.1117/12.2318930

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