A Remote Sensing Image Feature Extraction Method Based on Scatter Degree and Support Vector Machine
Aiming at the shortcomings of feature selection algorithm based on discretization,such as not being able to determine the feature threshold automatically and not being able to create new features,a remote sensing image feature extraction algorithm based on discretization and support vector machine is proposed.The algorithm first de-correlates the original feature library,then uses the optimized discretization index for feature selection,next extracts new features with better discriminative properties using the linear indivisible support vector machine model with the feature selection results,and uses the decision function as the feature threshold.The results of comparative experiments using UAV data from photovoltaic sites in Long county,Shaanxi show that the overall accuracy of classification using the new algorithm is 93.5％,and the Kappa coefficient is 0.9,which is 7％and 0.1 higher than that of the discretization-based feature extraction algorithm,and there is a certain degree of improvement in the accuracy of the producer and the accuracy of the user for each place,which is a better method of constructing the classification rule set.
scatter degreeSVMfeature extractionfeature selectionobject-orientedphotovoltaic site
万方数据
维普
