25 October 2016 Land cover classification based on object-oriented with airborne lidar and high spectral resolution remote sensing image
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Proceedings Volume 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology; 101560B (2016) https://doi.org/10.1117/12.2244142
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
In order to improve land cover classification accuracy of the coastal tidal wetland area in Dafeng, this paper take advantage of hyper-spectral remote sensing image with high spatial resolution airborne Lidar data. The introduction of feature extraction, band selection and nDSM models to reduce the dimension of the original image. After segmentation process that combining FNEA segmentation with spectral differences segmentation method, the paper finalize the study area through the establishment of the rule set classification of land cover classification. The results show that the proposed classification for land cover classification accuracy has improved significantly, including housing, shadow, water, vegetation classification of high precision. That is to say that the method can meet the needs of land cover classification of the coastal tidal wetland area in Dafeng. This innovation is the introduction of principal component analysis, and the use of characteristic index, shape and characteristics of various types of data extraction nDSM feature to improve the accuracy and speed of land cover classification.
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Fangfang Li, Fangfang Li, Zhengjun Liu, Zhengjun Liu, Qiangqiang Xu, Qiangqiang Xu, Haicheng Ren, Haicheng Ren, Xingyu Zhou, Xingyu Zhou, Yonghua Yuan, Yonghua Yuan, } "Land cover classification based on object-oriented with airborne lidar and high spectral resolution remote sensing image", Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 101560B (25 October 2016); doi: 10.1117/12.2244142; https://doi.org/10.1117/12.2244142
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