A kernel-based block matrix decomposition approach for the classification of remotely sensed images

The classification problem of remotely sensed images with hyperspectral and hyperspatial resolution images is being paid more and more attention. The success of remotely sensed images classification depends on many facts, such as the availability of high-quality images and ancillary data, proper classification procedure, and the analytical ability of scientific researcher. Therefore, lots of methods of combing spatial, spectral and texture information were proposed. However, these methods may ignore these facts as below. On the one hand, many details of the original remotely sensed images may be covered up by the too much overlapping information. On the other hand, the classification process is time-consuming. Therefore, a new and efficient classification of remotely sensed images method is introduced to overcome these shortcomings. The proposed method deals with the original information provided by the remotely sensed images is considered. The block matrix is made of training samples of the same class. The details of original remotely sensed images is obtained from the QR decomposition with column pivoting (QRcp) or singular value decomposition (SVD). And then, using fisher linear discriminant analysis (FLDA) methods, the projection data information of original remotely sensed images is jointly used for the classification through a support vector machines (SVMs) formulation. Experiments on hyperspatial and hyperspectral images are performed to test and evaluate the effectiveness of the proposed method.