Cloud detection from remotely observed data is a critical pre-processing step for various remote sensing applications. In particular, this problem becomes even harder for RGB color images, since there is no distinct spectral pattern for clouds, which is directly separable from the Earth surface. In this paper, we adapt a deep pyramid network (DPN) to tackle this problem. For this purpose, the network is enhanced with a pre-trained parameter model at the encoder layer. Moreover, the method is able to obtain accurate pixel-level segmentation and classification results from a set of noisy labeled RGB color images. In order to demonstrate the superiority of the method, we collect and label data with the corresponding cloud non-cloudy masks acquired from low-orbit Gokturk-2 and RASAT satellites. The experimental results validates that the proposed method outperforms several baselines even for hard cases (e.g. snowy mountains) that are perceptually difficult to distinguish by human eyes.
Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is designed to extract the multiscale features from each superpixel and predict the superpixel as one of three classes including thick cloud, thin cloud, and noncloud. Finally, the predictions of all the superpixels in the image yield the cloud detection result. In the proposed cloud detection framework, the improved SLIC method can obtain accurate cloud boundaries by optimizing initial cluster centers, designing dynamic distance measure, and expanding search space. Moreover, different from traditional cloud detection methods that cannot achieve multilevel detection of cloud, the designed deep CNN model can not only detect cloud but also distinguish thin cloud from thick cloud. Experimental results indicate that the proposed method can detect cloud with higher accuracy and robustness than compared methods. Automatic cloud detection from satellite imagery is a necessary preprocessing step in remote sensing. Given that humans can easily “see” clouds in an image because of salient region features, we adopt a visual attention technique in computer vision to automatically identify images with a significant cloud cover. The proposed method generates a rough cloud mask by using a top-down visual saliency model to qualitatively distinguish cloud images from noncloud images. First, an image is downsized for rapid processing. Some basic saliency maps of clouds are then generated by multilevel segmentation, the computation of cloud visual saliency features, and feature classification. Thereafter, we fuse the basic saliency maps by using a most-votes-win strategy to generate the cloud mask. With the cloud mask, a threshold is used to classify the images as cloud or noncloud images. A total of 200 RapidEye images are tested by using the algorithm. Of the cloud images, 92 are correctly identified. The average processing time is 1.8 s per image.