Abstract
Cloud detection of remote sensing imagery is quite challenging due to the influence of complicated underlying surfaces and the variety of cloud types. Currently, most of the methods mainly rely on prior knowledge to extract features artificially for cloud detection. However, these features may not be able to accurately represent the cloud characteristics under complex environment. In this paper, we adopt an innovative model named Fuzzy Autoencode Model (FAEM) to integrate the feature learning ability of stacked autoencode networks and the detection ability of fuzzy function for highly accurate cloud detection on remote sensing imagery. Our proposed method begins by selecting and fusing spectral, texture, and structure information. Thereafter, the proposed technique established a FAEM to learn the deep discriminative features from a great deal of selected information. Finally, the learned features are mapped to the corresponding cloud density map with a fuzzy function. To demonstrate the effectiveness of the proposed method, 172 Landsat ETM+ images and 25 GF-1 images with different spatial resolutions are used in this paper. For the convenience of accuracy assessment, ground truth data are manually outlined. Results show that the average RER (ratio of right rate and error rate) on Landsat images is greater than 29, while the average RER of Support Vector Machine (SVM) is 21.8 and Random Forest (RF) is 23. The results on GF-1 images exhibit similar performance as Landsat images with the average RER of 25.9, which is much higher than the results of SVM and RF. Compared to traditional methods, our technique has attained higher average cloud detection accuracy for either different spatial resolutions or various land surfaces.
Highlights
With the existence of clouds, solar radiation cannot or can hardly arrive at the land surface, which leads to the missing information and spectral distortion, and hinders the further image application [1,2]
The samples should belong to the pure objects such as thick cloud, water, building, vegetation and so on, the thin cloud which is the mixture of cloud and ground objects is not selected as samples
To demonstrate the efficiency of proposed model, both Landsat ETM+ and GF-1 images with different spatial resolution are applied in this experiment
Summary
With the existence of clouds, solar radiation cannot or can hardly arrive at the land surface, which leads to the missing information and spectral distortion, and hinders the further image application [1,2]. Cloud detection plays an indispensable role in image pre-processing. There are various clouds with different spectral characteristics. Some objects with high reflectance (such as snow, ice, etc.) are always confused with clouds. Optical thin clouds are difficult to detect as their spectral signal includes both clouds and the surface underneath [3]
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