Sub-pixel mapping method based on ANN and super-resolution reconstructed model
结合超分辨率重建的神经网络亚像元定位方法

Mixed pixels are always the case in remote sensed images, and how to analysis and explain mixed pixels is of importance in remote sensing applications. Sub-pixel mapping is a technique designed to obtain the spatial distribution of the classes inside the pixels with information of different endmembers to improve the accuracy of the classification. In this paper, a new BPMAP model is introduced by combination of the neural network and super-resolution reconstructed technology. The spatial distribution of the sub-pixel can be determined by establishing of observation model between the high-resolution and the low-resolution images after the neural network mapping; with restricted by Maximum A Posteriori (MAP) algorithm. The proposed model was tested on both simple synthetic image and ETM image in the three Gorges area. Results indicate that this method can mapping sub-pixel efficiently, and better performance was observed compared to that of the original ANN model.