Name
Title | ||
|---|---|---|
Unsupervised Subpixel Mapping of Remotely Sensed Imagery Based on Fuzzy C-Means Clustering Approach |
Abstract | ||
|---|---|---|
Subpixel mapping (SPM) is a technique to obtain a land cover map with finer spatial resolution than the original remotely sensed imagery. An image-based SPM model that directly uses the original image data as input by integrating both the spectral and spatial information has been demonstrated as a promising SPM model. However, all existing image-based SPM models are based on a supervised approach, since the spectral term in these SPM models is composed of a supervised unmixing method. The endmembers and training samples for different land cover classes must be determined before implementing these supervised SPM algorithms. In this letter, a novel unsupervised image-based SPM model based on the fuzzy c-means (FCM) clustering approach (usFCM_SPM) was proposed. By incorporating the unsupervised unmixing criterion of the FCM clustering algorithm and the maximal land cover spatial-dependence principle, the proposed usFCM_SPM can generate a subpixel land cover map without any prior endmember information. Both synthetic multispectral image and real IKONOS image experiments demonstrate that the usFCM_SPM can generate higher accuracy subpixel land cover maps than the traditional unsupervised pixel-scale classification approaches and the unsupervised pixel-swapping model. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LGRS.2013.2285404 | IEEE Geosci. Remote Sensing Lett. |
Keywords | Field | DocType |
real ikonos image experiments,terrain mapping,unsupervised subpixel mapping,spectral information,unsupervised subpixel mapping (spm),training samples,unsupervised unmixing,unsupervised image-based spm model,unsupervised pixel-swapping model,subpixel land cover map,spatial information,synthetic multispectral image,maximal land cover spatial-dependence principle,fuzzy logic,fuzzy c-means (fcm) clustering,geophysical image processing,remotely sensed imagery,supervised approach,fuzzy c-means clustering approach,finer spatial resolution,supervised unmixing method,spm technique,endmembers,usfcm_spm,spatial resolution,remote sensing,accuracy,clustering algorithms,indexes | Spatial analysis,Endmember,Remote sensing,Artificial intelligence,Subpixel rendering,Cluster analysis,Land cover,Computer vision,Pattern recognition,Fuzzy logic,Multispectral image,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
11 | 5 | 1545-598X |
Citations | PageRank | References |
4 | 0.41 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihang Zhang | 1 | 86 | 8.80 |
| Yun Du | 2 | 153 | 16.11 |
| Xiaodong Li | 3 | 171 | 16.82 |
| Shiming Fang | 4 | 51 | 2.49 |
| Feng Ling | 5 | 209 | 21.29 |