期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2000
卷号：XXXIII Part B7(/1-4)
页码：640-645
出版社：Copernicus Publications
摘要：Several commercial companies that plan to provide improved panchromatic and/or multi-spectral remote sensor data in the near future are suggesting that ' merge' datasets will be of significant value. This study evaluated the utility of one major merging process-process co mponents analysis and its inverse. The 6 bands of 30 30m Landsat TM data and the 10 10m SPOT panchromatic data were used to create a new 10 10m merged data file. For the image classification, 6 bands that is 1st, 2nd, 3rd, 4th, 5th and 7th band may be used in conjunction with sup ervised classification algorithms except band 6. One of the 7 bands is Band 6 that records thermal IR energy and is rarely used because of its coarse spatial resolut ion (120m) except being employed in thermal mapping. Because SPOT panchromatic has high resolution, it makes 10 10m SPOT panchromatic data be used to classify for the detailed classification. SPOT as the Landsat has acquired hundreds of thousands of images in digital format that are commercially available and are used by scientists in different fields. After the merged, the classifications used supervised classification and neural network. The method of the supervised classification is what used parallelepiped and/or minimum distance and MLC (Maximum Likelihood Classification). The back-propagation in the multi-layer perception is one of the neural networks. The used method in this paper is MLC (Maximum Likelihood Classification) of the supervised classification and the back-propagation of the neural network. Later in this research SPOT systems and images are co mpared with these classification. A comparative analysis of the classifications from the TM and the merged SPOT/TM datasets will be resulted in some conclusions. As a result, the overall accuracy at the MLC of the Merging Image was 86.972% with a KHAT of 0.830 and it at the MLC of only TM was 87.242%, while a KHAT was 0.834. But the overall accuracy at the B.P of the neural net works was 87.781% with a KHAT of 0.841 and the TM was 86.253%, while a KHAT was 0.821
