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Automatic cloud extraction from satellite imagery is a vital process for many applications in optical remote sensing since clouds can locally obscure the surface features and alter the reflectance. Clouds can be easily distinguished by the human eyes in satellite imagery via remarkable regional characteristics, but finding a way to automatically detect various kinds of clouds by computer programs to speed up the processing efficiency remains a challenge. This paper introduces a new cloud detection method based on probabilistic latent semantic analysis (PLSA) and object-based machine learning. The method begins by segmenting satellite images into superpixels by Simple Linear Iterative Clustering (SLIC) algorithm while also extracting the spectral, texture, frequency and line segment features. Then, the implicit information in each superpixel is extracted from the feature histogram through the PLSA model by which the descriptor of each superpixel can be computed to form a feature vector for classification. Thereafter, the cloud mask is extracted by optimal thresholding and applying the Support Vector Machine (SVM) algorithm at the superpixel level. The GrabCut algorithm is then applied to extract more accurate cloud regions at the pixel level by assuming the cloud mask as the prior knowledge. When compared to different cloud detection methods in the literature, the overall accuracy of the proposed cloud detection method was up to 90 percent for ZY-3 and GF-1 images, which is about a 6.8 percent improvement over the traditional spectral-based methods. The experimental results show that the proposed method can automatically and accurately detect clouds using the multispectral information of the available four bands.

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CMR-TT FLS and STE GLS showed potential to provide rapid assessment of RV function and had superior correlation with RVEF compared to conventional parameters.