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• W4383428058 abstract "In Remote Sensing Image (RSI) pre-processing steps, detecting and removing cloudy areas is a critical task. Recently, cloud detection methods based on deep neural networks achieve outstanding performance over traditional methods. Current approaches mostly focus on cloud detection on a single image captured by polar-orbiting satellites. However, there is another type of meteorological satellite - geostationary satellite, which can capture temporal consecutive frames of a particular location. Therefore, the cloud detection task targeting at geostationary satellite can be treated as a video cloud detection task. And in addition to extracting features on a single image, extracting and making full use of the relations between sequential frames is also important. To tackle this problem, we design a deep learning video cloud detection model: Transformer Network for Video Cloud Detection (TRCDNet). The proposed network is based on the encoder-decoder structure. In the encoder, the module ContextGhostLayer is proposed to encode more semantic information to tackle the challenging problems like thin cloud in RSIs. Besides, we design a transformer-based Video Sequence Transformer (VSTR) block. Based on attention mechanism, VSTR can fully extract the across-frame relations. In the proposed decoder, the cloud masks are recovered gradually to the same scale as the input image. To evaluate the methods, we create a Video Cloud Detection dataset based on the captured videos from Fengyun 4 (FY-4) satellite: Fengyun4aCloud. Extensive experiments of current cloud detection methods, semantic segmentation methods, and video semantic segmentation methods indicate that the designed TRCDNet achieves state-of-art performance in video cloud detection." @default.
• W4383428058 created "2023-07-07" @default.
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• W4383428058 date "2023-01-01" @default.
• W4383428058 modified "2023-09-26" @default.
• W4383428058 title "TRCDNet: A Transformer Network for Video Cloud Detection" @default.
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• W4383428058 doi "https://doi.org/10.1109/tgrs.2023.3288543" @default.
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