摘要
用不同空间分辨率的 TM与 IRS- 1C(PAN)遥感图像进行融合 ,可增强图像清晰度。本研究用人工神经网络 BP算法对 TM和 IRS- 1C(PAN)的融合图像进行土地覆盖分类 ,分类的总体精度达到 95 % ,高于最大似然法 (分类的总体精度为 71% )
The fused product merged two optical image data of different resolutions--a high spatial resolution panchromatic image (IRS 1C) and a low spatial resolution but multispectral image (TM). Its signal clarity was improved. Artificial neural network technology is of great advantage to deal with data of uncertain distributing and qualitative data such as performing non linear classification, and thus being used to classify the land cover. The classification accuracy of fused remote sensing image reached a accuracy of up to 95%. It is far much better than the method of maximum likelihood classification, whose total accuracy is only 71%.
出处
《中国农业大学学报》
CAS
CSCD
北大核心
2001年第5期76-80,共5页
Journal of China Agricultural University
基金
北京市土地变更调查攻关课题资助
