Study on Remote Sensing Image Classification of Oasis Area Based on ENVI Deep Learning

In this paper, based on the Landsat multispectral remote sensing images of 1999, 2008 and 2019 in the oasis area of the Taolai River Basin, a remote sensing image classification method based on ENVI deep learning was constructed to extract and identify the cover information of oasis area on the basis of establishing classification system, interpretation flags and sample data sets, and compared with the classification methods based on backpropagation neural network (BPNN), support vector machine regression (SVM) and random forest (RF). The results show that the overall accuracy of the classification method based on ENVI deep learning is 97.34 %, and the Kappa coefficient is 0.96; Under the same number of samples, compared with the classification method based on BPNN, SVM and RF, the classification method based on ENVI deep learning constructed in this study improves the overall accuracy by 6.80%, 2.04% and 3.03%, and the Kappa coefficient increases by 0.12, 0.07 and 0.09, respectively, and the classification method is the best for extracting surface cover information fin oasis area. This study can provide technical support for rapid and accurate extraction and identification of ground cover information.