Hyperspectral Images Classification Using Energy Profiles of Spatial and Spectral Features
This work addresses classification accuracy for remote sensing applications, but it is incremental as it builds on existing methods with a new filter design.
The paper tackled hyperspectral image classification by proposing a spatial feature extraction method based on energy profiles, which improved classification results on Indian Pines and Salinas datasets compared to recent spectral-spatial methods.
This paper proposes a spatial feature extraction method based on energy of the features for classification of the hyperspectral data. A proposed orthogonal filter set extracts spatial features with maximum energy from the principal components and then, a profile is constructed based on these features. The important characteristic of the proposed approach is that the filter sets coefficients are extracted from statistical properties of data, thus they are more consistent with the type and texture of the remotely sensed images compared with those of other filters such as Gabor. To assess the performance of the proposed feature extraction method, the extracted features are fed into a support vector machine (SVM) classifier. Experiments on the widely used hyperspectral images namely, Indian Pines, and Salinas data sets reveal that the proposed approach improves the classification results in comparison with some recent spectral spatial classification methods.