Weakly Supervised Classification of Remotely Sensed Imagery Using Label Constraint and Edge Penalty

摘要

The classification of pixels in remotely sensed imagery (RSI) into land cover classes typically requires knowing the labels of some image pixels for model training. However, accurate pixel-level label information is usually difficult and expensive to acquire, which restricts the applicability of supervised image classification methods. In contrast, the region labels information that specifies which classes are contained in a region of the image that is easier to acquire and less susceptible to identification errors. To utilize the region label information for remotely sensed image classification, this paper presents a weakly supervised image classification approach using label constraint and edge penalty (ILCEP), which has the following key characteristics. First, the predefined region labels are used as constraints in ILCEP to guide the inference of pixel labels in the image. Second, the edges between neighboring pixels are used as penalties to address the spatial contextual information in the image. Third, the label constraint and edge penalty are incorporated into the conditional random field framework, and simultaneous model learning and label inference are achieved by solving the maximum a posteriori problem through an enhanced simulated annealing algorithm. Experiments on both simulated and real RSIs demonstrate that the proposed approach can achieve high classification accuracy by knowing only the region-level label information.