期刊
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷 158, 期 -, 页码 63-75出版社
ELSEVIER
DOI: 10.1016/j.isprsjprs.2019.09.018
关键词
Landform recognition; Multi-modal geomorphological data fusion; Deep learning; Convolutional neural networks (CNN)
类别
资金
- National Natural Science Foundation of China [41871322, 61772425, 41930102]
- China's National Key RD Plan [2017YFB0503503]
Automatic landform recognition is considered to be one of the most important tools for landform classification and deepening our understanding of terrain morphology. This paper presents a multi-modal geomorphological data fusion framework which uses deep learning-based methods to improve the performance of landform recognition. It leverages a multi-channel geomorphological feature extraction network to generate different characteristics from multi-modal geomorphological data, such as shaded relief, DEM, and slope and then it harvests joint features via a multi-modal geomorphological feature fusion network in order to effectively represent landforms. A residual learning unit is used to mine deep correlations from visual and physical modality features to achieve the final landform representations. Finally, it employs three fully-connected layers and a softmax classifier to generate labels for each sample data. Experimental results indicate that this multi-modal data fusion-based algorithm obtains much better performance than conventional algorithms. The highest recognition rate was 90.28%, showing a great potential for landform recognition.
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