Name
Abstract | ||
|---|---|---|
Understanding a scene provided by very high resolution (VHR) satellite imagery has become a more and more challenging problem. In this paper, we propose a new method for scene classification based on saliency computing of patches sampling from the VHR images. Sparse principal component analysis (sPCA) is then adopted to select the corresponding informative salient patches for image scene representation. The proposed technique for selecting informative salient patches is efficient and robust for scene understanding. We conduct experiments on the public UC Merced benchmark dataset, which contains 21 different areal categories with sub-meter resolution. Experimental results demonstrate the effectiveness of the proposed method, as compared with several state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGARSS.2016.7729708 | 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) |
Keywords | Field | DocType |
Scene classification, saliency detection, sparse principal component analysis, feature selection | Computer vision,Sparse PCA,Satellite imagery,Pattern recognition,Computer science,Salience (neuroscience),Visualization,Feature extraction,Artificial intelligence,Image resolution,Principal component analysis,Salient | Conference |
ISSN | Citations | PageRank |
2153-6996 | 2 | 0.38 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Souleyman Chaib | 1 | 20 | 1.27 |
| Yanfeng Gu | 2 | 742 | 55.56 |
| Hongxun Yao | 3 | 2485 | 156.65 |
| Sicheng Zhao | 4 | 858 | 50.46 |