期刊
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 39, 期 19, 页码 6281-6299出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2018.1458346
关键词
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资金
- National Natural Science Foundation of China [41501451, 41701491]
- Fujian Province Education Research Project for Young and Middle-aged Teachers [JAT160087, JAT160070]
- Visiting Scholar Program of Fujian Province
Classifying land-use scenes from high-resolution remote-sensing imagery with high quality and accuracy is of paramount interest for science and land management applications. In this article, we proposed a new model for land-use scene classification by integrating the recent success of convolutional neural network (CNN) and constrained extreme learning machine (CELM). In the model, the fully connected layers of a pretrained CNN have been removed. Then, CNN works as a deep and robust convolutional feature extractor. After normalization, deep convolutional features are fed to the CELM classifier. To analyse the performance, the proposed method has been evaluated on two challenging high-resolution data sets: (1) the aerial image data set consisting of 30 different aerial scene categories with sub-metre resolution and (2) a Sydney data set that is a large high spatial resolution satellite image. Experimental results show that the CNN-CELM model improves the generalization ability and reduces the training time compared to state-of-the-art methods.
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