Name
Title | ||
|---|---|---|
A noise removal approach for object-based classification of VHR imagery via post-classification |
Abstract | ||
|---|---|---|
The pixel-based classification of remotely sensed images always produces a large amount of “speckled” or “salt and pepper” noises. Both post-classification smoothing and object-based classification techniques have been proposed to tackle this problem. However, most of them are not adequate to deal with the noises in object-based classification of very high resolution (VHR) remote sensing imagery, because a lot of noisy regions will be produced by image segmentation and the existing post-classification approaches generally are tailored towards pixel-based classification. This paper proposes a novel noise removal approach for object-based classification of VHR imagery via post-classification. It includes four phases: firstly, an image is segmented into homogeneous regions; secondly, all regions are classified according to their spectral and texture features; thirdly, noisy regions are distinguished by using shape features. Finally, the noisy regions are removed by using contextual features. Experimental results show the proposed approach is effective and can improve the overall accuracy of classification of VHR remote sensing imagery. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICALIP.2014.7009928 | Audio, Language and Image Processing |
Keywords | DocType | Citations |
feature extraction,geophysical image processing,image classification,image denoising,image resolution,image segmentation,image texture,remote sensing,vhr imagery,vhr remote sensing imagery,noise removal approach,object-based classification techniques,pixel-based classification,post-classification approaches,post-classification smoothing,remotely sensed images,salt and pepper noises,speckled noises,spectral features,texture features,very high resolution remote sensing imagery,noise removal,object-based classification,post-classification,noise measurement,shape,artificial intelligence,accuracy,noise | Conference | 1 |
PageRank | References | Authors |
0.36 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laiwen Zheng | 1 | 9 | 1.15 |
| Lihong Wan | 2 | 12 | 3.54 |
| Hong Huo | 3 | 2 | 1.16 |
| Tao Fang | 4 | 226 | 31.10 |