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Optical satellite images frequently suffer from thin clouds, degrading the data quality. A thin cloud correction method is developed based on complementary dark objects on multiple visible bands to address this problem. First, thin cloud images are divided into irregular subareas using the superpixel segmentation algorithm, enabling the proper identification of dark objects in the spatial domain across different visible bands. A criterion is then established to classify the dark objects into two types, namely, absolute dark objects (ADOs) and relative dark objects (RDOs). Subsequently, the quantitative correlation of thin clouds between visible bands is estimated by adopting the ADOs. Dark objects present complementarity in visible bands; thus, the RDOs on one band are spatially densified by referencing the RDOs on the other visible bands. Thereby, a thin cloud map with fine spatial details is interpolated by using all the ADOs and RDOs on a band, and the correction procedure is performed through subtraction. Eight visible data captured by Landsat platforms are collected for simulated and real experiments to evaluate the method's performance. Three representative thin cloud correction approaches are selected for visual and quantitative comparisons. The proposed method can correct thin clouds effectively and restore various scenes accurately. The interpolated thin cloud maps show enhanced texture details and finer representation compared with the benchmarks. In addition, the advantages of dark-object densification for thin cloud map generation and the limitations of the proposed method are investigated.

Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road centerline extraction. [ABSTRACT FROM PUBLISHER]