RESUMEN
In the field of remote sensing image processing, the classification of hyperspectral image (HSI) is a hot topic. There are two main problems lead to the classification accuracy unsatisfactory. One problem is that the recent research on HSI classification is based on spectral features, the relationship between different pixels has been ignored; the other is that the HSI data does not contain or only contain a small amount of labeled data, so it is impossible to build a well classification model. To solve these problems, a dual-channel CNN model has been proposed to boost its discriminative capability for HSI classification. The proposed dual-channel CNN model has several distinct advantages. Firstly, the model consists of spectral feature extraction channel and spatial feature extraction channel; the 1-D CNN and 3-D CNN are used to extract the spectral and spatial features, respectively. Secondly, the dual-channel CNN have been used for fusing the spatial-spectral features, the fusion feature is input into the classifier, which effectively improves the classification accuracy. Finally, due to considering the spatial and spectral features, the model can effectively solve the problem of lack of training samples. The experiments on benchmark data sets have demonstrated that the proposed dual-channel CNN model considerably outperforms other state-of-the-art method.
RESUMEN
The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX) detector and its parallel implementation on graphics processing units (GPUs). The WSSKRX utilizes the spatial neighborhood resources to reconstruct the testing pixels by introducing a spectral factor and a spatial window, thereby effectively reducing the interference of background noise. Then, the kernel function is redesigned as a mapping trick in a KRX detector to implement the anomaly detection. In addition, a powerful architecture based on the GPU technique is designed to accelerate WSSKRX. To substantiate the performance of the proposed algorithm, both synthetic and real data are conducted for experiments.