Name
Abstract | ||
|---|---|---|
Change detection was one of the earliest and is also one of the most important applications of remote sensing technology. For multispectral images, an effective solution for the change detection problem is to exploit all the available spectral bands to detect the spectral changes. However, in practice, the temporal spectral variance makes it difficult to separate changes and nonchanges. In this paper, we propose a novel slow feature analysis (SFA) algorithm for change detection. Compared with changed pixels, the unchanged ones should be spectrally invariant and varying slowly across the multitemporal images. SFA extracts the most temporally invariant component from the multitemporal images to transform the data into a new feature space. In this feature space, the differences in the unchanged pixels are suppressed so that the changed pixels can be better separated. Three SFA change detection approaches, comprising unsupervised SFA, supervised SFA, and iterative SFA, are constructed. Experiments on two groups of real Enhanced Thematic Mapper data sets show that our proposed method performs better in detecting changes than the other state-of-the-art change detection methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TGRS.2013.2266673 | IEEE T. Geoscience and Remote Sensing |
Keywords | Field | DocType |
change detection,change detection problem,remote sensing,multitemporal images,image transformation,slow feature analysis (sfa),spectral bands,unchanged pixels,spectral changes,data transform,feature extraction,geophysical image processing,enhanced thematic mapper data sets,slow feature analysis change detection approaches,multispectral imagery,iterative slow feature analysis,feature space,temporal spectral variance,unsupervised slow feature analysis,iterative methods,remote sensing technology | Data set,Change detection,Remote sensing,Artificial intelligence,Spectral bands,Computer vision,Feature vector,Pattern recognition,Feature (computer vision),Multispectral image,Feature extraction,Pixel,Mathematics | Journal |
Volume | Issue | ISSN |
52 | 5 | 0196-2892 |
Citations | PageRank | References |
28 | 1.14 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chen Wu | 1 | 46 | 6.11 |
| Bo Du | 2 | 1662 | 130.01 |
| Liangpei Zhang | 3 | 5448 | 307.02 |