Crop semivariogram texture character analysis and classification from ERS-2 SAR image

摘要

This paper uses semivariogram to quantify the crop spatial pattern from ERS-2 SAR image, especially for the cotton field, to improve the extraction accuracy for cotton growth monitoring. Measuring the influence of the semivariogram calculation variable can understand and control the calculation variable for remote sensing classification better. The crops semivariograms of study area exhibit a similar bounded shape resulting the regularization effect, the sill reaches at about 12 pixel, 150 m, the mean size of agricultural fields in the studied area. In this agricultural landscape, spatial structure results mainly from cultivation patterns. The cotton and maize semivariograms are quite different distinctively. The semivariogram of each class reflects the texture characters, it measures the each class spatial structure and similarity relative to the size and direction of calculation window, which has different effect on the results of classification. We can select the window size according to the range of each class. Joining the classification with the average value for the four direction semivariograms can reduce the band numbers and classification time and elevate the accuracy. The results in study area indicate combining average semivariogram and spectrum in classification elevates 12.4% on overall accuracy compared to spectrum only.