Multi-source remotely sensed data fusion for improving land cover classification
Publication in refereed journal

香港中文大學研究人員
替代計量分析
.

其它資訊
摘要Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.
出版社接受日期20.12.2016
著者CHEN Bin, HUANG Bo, XU Bing
期刊名稱ISPRS Journal of Photogrammetry and Remote Sensing
出版年份2017
月份2
卷號124
頁次27 - 39
國際標準期刊號0924-2716
電子國際標準期刊號1872-8235
語言美式英語
關鍵詞Land cover classification, Remote sensing, Data fusion, Temporal and angular features

上次更新時間 2020-07-08 於 00:27