Name
Abstract | ||
|---|---|---|
Hyperspectral image (HSI) classification is a very active research topic in remote sensing and has numerous potential applications. This paper presents a simple but effective classification method based on spectral-spatial information and K-nearest neighbor (KNN). To be specific, we propose a spectral-spatial KNN (SSKNN) method to deal with the HSI classification problem, which effectively exploits the distances all neighboring pixels of a given test pixel and training samples. In the proposed SSKNN framework, a set-to-point distance is exploited based on least squares and a weighted KNN method is used to achieve stable performance. By using two standard HSI benchmark, we evaluate the proposed method by comparing it with eight competing methods. Both qualitative and quantitative results demonstrate our SSKNN method achieves better performance than other ones. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s11042-017-4403-9 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Hyperspectral image classification,KNN,Spectral-spatial,Joint model | Hyperspectral image classification,Least squares,k-nearest neighbors algorithm,Data mining,Pattern recognition,Computer science,Hyperspectral imaging,Pixel,Artificial intelligence | Journal |
Volume | Issue | ISSN |
77 | 9 | 1380-7501 |
Citations | PageRank | References |
2 | 0.36 | 41 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunjuan Bo | 1 | 43 | 4.98 |
| Huchuan Lu | 2 | 4827 | 186.26 |
| Dong Wang | 3 | 326 | 14.06 |