Crop Mapping of Complex Agricultural Landscapes Based on Discriminant Space

Abstract

Crop mapping is related to planting conditions, remote sensing data source, and performances of various classifiers. Considering the above key factors, this study discusses a crop mapping approach for complex agricultural landscapes based on a conceptual frame that is called discriminant space. The introduced discriminant space-based model follows the pattern and process rules in geosciences and is found by the variables closely related to crop growth. On this basis, a method of classification based on probability calibration algorithm (CPCA) is developed, which consists of several steps. First, discriminant variables were extracted and then the experimental discriminant space was founded. Spectral analysis was carried out in this process to select the dominant impact factors for crop mapping. Based on the founded discriminant space, we calculated the class-conditional probabilities for each category using kernel density estimation and separated the residuals of class-conditional probabilities from the structural component through a trend-surface analysis. Then, calibration of the class-conditional probabilities was implemented using the predicted residuals generated by simple kriging with varying local means. Classification was achieved according to the calibrated probabilities with respect to each category. Accuracy assessment and kappa analysis were used to evaluate the identification results. Statistical test procedure was applied to measure the differences between classification maps and the validation data based on individual categories. Results are discussed for an intensively cropped area in south China. Compared with two other supervised classifiers, the overall accuracy of the proposal method increases by more than 10% and is able to achieve 87.12%. Moreover, there are some substantial increases in the producer's accuracy and the user's accuracy of single-cropping rice and late rice, with a precision increase of more than 15%, effectively improves the identification accuracy of rice in the research region. The proposed method overcomes the limitations due to spectrum characteristics and considers the structural and the random properties of spatial distribution for categories in local regions. Therefore, a similar operation can usually be implemented for crop identification of complex agricultural landscapes.