Multispectral subpixel detection using least square unmixing

Hsuan Ren, Yang Lang Chang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

3 引文 斯高帕斯(Scopus)


Many approaches have been developed for subpixel target detection in the past, and least square unmixing is one of the most widely used methods. It can detect subpixel target by estimating its abundance fraction resident in each pixel. This method has been successfully applied in hyperspectral remotely sensed images, but in order for this approach to be effective, the number of bands must be no less than that of signatures to be classified. This constraint is known as Band Number Constraint (BNC). Such inherent constraint is not an issue for hyperspectral images since they generally have hundreds of bands, which is more than the number of signatures resident within images. However, this may not be true for multispectral images where the number of signatures to be classified is greater than the number of bands. In this paper, instead of increasing the number of bands, we decrease the number of signatures by selecting part of materials applied to least square approach, and then those detection results are nonlinearly combined for endmember detection. It can be viewed as an extension of the least square approach and the experimental results showed it can successfully detect all endmembers.

主出版物標題2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(列印)0780395107, 9780780395107
出版狀態已出版 - 2006
事件2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS - Denver, CO, United States
持續時間: 31 7月 20064 8月 2006


名字International Geoscience and Remote Sensing Symposium (IGARSS)


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國家/地區United States
城市Denver, CO


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