Spoon network: a new network structure for Landsat imagery cloud detection

doi:10.7523/j.ucas.2020.0036

Abstract

Abstract: In view of the shortcomings of the neural network model for remote sensing image cloud detection, such as the loss of detail information, the high cloud miss detection rate and the complexity of calculation caused by the insufficient utilization of spectral information, this paper proposes a new and lightweight network called spoon net (S-Net), which is applied to the cloud detection of Landsat remote sensing image. S-Net is divided into two stages. In the first stage, the convolution kernel of 1×1 is used to extract image spectral features to avoid image details being blurred; in the second stage, the encoder decoder framework is used to extract image spatial features, and group convolution is introduced to convolute each layer of spectral channels extracted in the first stage separately to maintain spectral features and reduce model parameters. The model is trained and evaluated in Landsat8 biome dataset, and the results show that the model has a great advantage in memory and time, and achieves an accuracy of 95%.

Key words: Landsat, cloud detection, neural network, spectral characteristics, spoon-net

CLC Number:

TP751.2

WANG Shuli, TANG Hairong, JI Luyan. Spoon network: a new network structure for Landsat imagery cloud detection[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(5): 712-720.

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