Name
Abstract | ||
|---|---|---|
It is of great interest in spectral-spatial features classification for High spectral images (HSI) with high spatial resolution. This paper presents a new Spectral-spatial method for improving accuracy of hyperspectral image classification. Specifically, a new texture feature extraction algorithm based on traditional LBP method is proposed directly. Texture profiles is obtained by the proposed method. A composite kernel framework is employed to join spatial and spectral features. The classifiers adopted in this work is the multinomial logistic regression. In order to illustrate the good performance of the proposed framework, the two real hyperspectral image datasets are employed. Our experimental results with real hyperspectral images indicate that the proposed framework can enhance the classification accuracy than some traditional alternatives. |
Year | Venue | Field |
|---|---|---|
2018 | BICS | Hyperspectral image classification,Feature extraction algorithm,Pattern recognition,Multinomial logistic regression,Computer science,Hyperspectral imaging,Artificial intelligence,Composite kernel,Image resolution |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
11 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cailing Wang | 1 | 6 | 2.81 |
| hongwei wang | 2 | 36 | 8.68 |
| Jinchang Ren | 3 | 1144 | 88.54 |
| Yinyong Zhang | 4 | 3 | 0.75 |
| Jia Wen | 5 | 2 | 2.26 |
| Jing Zhao | 6 | 107 | 59.16 |