High Spatial Resolution Remote Sensing Image Classification Based on Deep Learning

A classification method based on deep learning is proposed for the classification of high spatial resolution remote sensing images. The texture features of the images are calculated through nonsubsampled contourlet transform, the deep learning common model-deep belief networks (DBN) are used to classify the high spatial resolution remote sensing images based on spectral and texture features. The proposed method is compared with the DBN classification method based on single spectral information, the support vector machine (SVM) method and the traditional neural network (NN) classification method. Experimental results show that comparing with the single spectral information, the use of spectral and texture information can effectively improve the classification accuracy of high spatial resolution remote sensing images, and comparing with methods of SVM and NN, the DBN method can accurately explore the distribution law of the high spatial resolution remote sensing images and improve the accuracy of classification.