Study on the potential of whitening transformation in improving single crop mapping accuracy

Abstract

In some practical classification applications, our focus may be just a specific crop (e.g., rice, corn, or soybean). Therefore, how to distinguish the crop of interest from other classes accurately and quickly is an issue worth studying. We propose an approach that combines whitening transformation (WT)/interest-class-based whitening transformation (ICWT) and one-class classification (OCC) methods to identify the crop of interest. The image created by applying WT/ICWT to the original image is referred to as the totally/partially whitened image, respectively. Three typical OCC classifiers—one-class support vector machine (OCSVM), positive and unlabeled learning (PUL), and maximum entropy (MaxEnt)—are trained to identify the crop of interest, using a totally/partially whitened image. The research results demonstrate that (1) both ICWT and WT enabled OCSVM to improve the producer’s accuracy of the crop of interest by more than 20%, and the performance of the proposed approach reached or surpassed that of conventional supervised methods; (2) PUL and MaxEnt performed better than OCSVM, especially in identifying local major crops using different phased data; and (3) the proposed approach is efficient in single crop identification with remote sensing imagery and is worth recommending for practical applications.