Scene Learning for Cloud Detection on Remote-Sensing Images

Cloud detection plays a major role for remote-sensing image processing. To accomplish the task, a novel automatic supervised approach based on the “scene-learning” scheme is proposed in this paper. Scene learning aims at training and applying a cloud detector on the whole image scenes. The cloud detector herein is a special classifier that is used to separate clouds from the backgrounds. Concretely, scene learning regards each pixel of scenes in training image as a sample, and uses it to train a cloud detector. Accordingly, the detecting process is also implemented on each pixel of testing image using the trained detector. Generally, scene-learning scheme contains two modules: feature data simulating and cloud detector learning and applying.

We first simulate a kind of cubic structural data (also named feature data) by stacking different fundamental image features, including color, statistical information, texture, and structure. Such data synthesize different image features, and it is used for cloud detector training and applying. Cloud detector is designed based on minimizing the residual error between the feature data and its labels. The detector is easy to be trained because of its closed-form. Applying the detector and some necessary cloud refinement methods to the testing images, we could finally detect clouds. We also theoretically analyze the influence of feature number and prove that more features lead to better performance of scene learning under certain circumstance. Comparisons of qualitative and quantitative analyses of the experimental results are implemented. Results indicate the efficacy of the proposed method.

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