Abnormal Training Samples Detection Method Based on Median Absolute Deviation

The supervised classification technology of remote sensing images is widely used in the field of information extraction and change detection in which the selection of training samples is very important and the quality of training samples directly determines the accuracy of classification However due to the limitation of conditions and human error some impure or wrong training samples may be selected resulting in a decrease in classification accuracy In order to solve this problem the median absolute deviation method is used to detect and eliminate impure and wrong training samples in the supervised classification of remote sensing images based on the spectral information of the image The optical remote sensing image data obtained from Landsat-8 in some areas of Nanchang city is selected the support vector machine is used to supervise and classify the two situations that contain abnormal training samples and eliminate abnormal training samples and compare the classification results Experimental results show that the classification accuracy of removing abnormal training samples is significantly better than that of abnormal training samples