ECDNet: A bilateral lightweight cloud detection network for remote sensing images. (September 2022)

• Record Type: Journal Article
• Authors: Luo, Chen; Feng, Shanshan; Li, Xutao; Ye, Yunming; Zhang, Baoquan; Chen, Zhihao; Quan, YingLing
• Is Part Of: Pattern recognition. Volume 129(2022)
• Publication Date: 2022-09
• Subjects: Lightweight network -- Efficient cloud detection -- Dual-branch architecture; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108713
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a neural network: ECDNet. It consists of a lightweight two-pathway encoder and an extremely lightweight decoder. In the encoder, the dense pyramid module (DPM) is designed to have large and diverse receptive fields in feature extraction. In the encoder, the fusion module (FM) is developed to fuse detail and semantic information more efficiently. The experiment results on LandSat8 and MODIS demonstrate that ECDNet can achieve state-of-the-art performance. Abstract: Cloud detection is one of the critical tasks in remote sensing image pre-processing and it has attracted extensive research interest. In recent years, deep neural networks based cloud detection methods have surpassed the traditional methods (threshold-based methods and conventional machine learning-based methods). However, current approaches mainly focus on improving detection accuracy. The computation complexity and large model size are ignored. To tackle this problem, we propose a lightweight deep learning cloud detection model: Efficient Cloud Detection Network (ECDNet). This model is based on the encoder-decoder structure. In the encoder, a two-path architecture is proposed to extract the spatial and semantic information concurrently. One pathway is the detail branch. It is designed to capture low-level detail spatial features with only a few parameters. The other pathway is the semantic branch, which is mainly for capturing context features. In the semantic branch, a proposed dense pyramid module (DPM) is designed for multi-scale contextual information extraction. The number of parameters and calculations in DPM is greatly reduced by features reusing. Besides, a FusionBlock is developed to merge these two kinds of information. Then the extreme lightweight decoder recovers the cloud mask to the same scale as the input image step by step. To improve performance, boost loss is introduced without inference cost increment. We evaluate the proposed method on two public datasets: LandSat8 and MODIS. Extensive experiments demonstrate that the proposed ECDNet achieves comparable accuracy as the state-of-art cloud detection methods, and meantime has a much smaller model size and less computation burden.