Abstract
Multispectral data and radar data contain abundant spectral and texture features, which are widely used in land use classification. High precision registration and information mining are the two major difficulties faced by the classification of these two data. In this paper, visible and near infrared spectrum range of Wide-band Imaging Spectrometer (VNI) and Interferometric Imaging Radar Altimeterdata (InIRA) are used as raw data, and data registration and combination is carried out (the combination data is called for VNI_InIRA). Then the information of each pixel before and after combination is extracted and transformed into gray-scale image which is easy to be input by convolution neural network (CNN). Finally, the classification of land use is carried out by trained CNN models, and the accuracy is verified. The results show that the classification results of VNI_InIRA data are better than those of VNI data. The overall classification accuracy is 94.29% and 91.83% respectively. Therefore, the CNN which synthesizes the spectral characteristics of VNI data and the texture topographic features of InIRA data is a feasible method to obtain accurate land use classification information, and to provide a reference for the study of land use classification extraction in coastal areas.
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Acknowledgement
Thanks to China Manned Space Engineering for providing space science and application data products of Tiangong-2. Thanks to the National R&D Infrastructure and Facility Development Program of China, “Fundamental Science Data Sharing Platform” (DKA2018-12-02-23) who funded the project.
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Liu, K., Zhou, Z., Wan, X., Hei, B., Xiong, S., Wu, C. (2019). Deep Learning Network Integrated Multi-spectral Data and Interferometric Imaging Radar Altimeter Data of Tiangong-2 for Land Use Classification. In: Gu, Y., Gao, M., Zhao, G. (eds) Proceedings of the Tiangong-2 Remote Sensing Application Conference. Lecture Notes in Electrical Engineering, vol 541. Springer, Singapore. https://doi.org/10.1007/978-981-13-3501-3_22
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DOI: https://doi.org/10.1007/978-981-13-3501-3_22
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