PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information

Wangbin Li,Kaimin Sun,Song Gao,Wenzhuo Li,Zhuotong Du,Xiuqing Hu,Jinjiang Wei

doi:10.3390/rs13183670

Abstract

Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activities, such as product retrieval, land-use/cover classification, object detection, and especially for change detection. However, the complexity of clouds themselves make it difficult to detect thin clouds and small isolated clouds. To accurately detect clouds in satellite imagery, we propose a novel neural network named the Pyramid Contextual Network (PCNet). Considering the limited applicability of a regular convolution kernel, we employed a Dilated Residual Block (DRB) to extend the receptive field of the network, which contains a dilated convolution and residual connection. To improve the detection ability for thin clouds, the proposed new model, pyramid contextual block (PCB), was used to generate global information at different scales. FengYun-3D MERSI-II remote-sensing images covering China with 14,165 × 24,659 pixels, acquired on 17 July 2019, are processed to conduct cloud-detection experiments. Experimental results show that the overall precision rates of the trained network reach 97.1% and the overall recall rates reach 93.2%, which performs better both in quantity and quality than U-Net, UNet++, UNet3+, PSPNet and DeepLabV3+.

Highlights

With the rapid development of remote-sensing technology, more and more remotesensing images are employed for farmland monitoring, land use, target detection and so on in production and supporting living [1]
Based on the issues of these methods and the distributions of clouds in images, we propose a new Pyramid Contextual Network (PCNet) using the global information at different scales comprehensively
Remote-sensing images from Landsat and MODIS are mostly investigated in clouddetection research

Summary

Introduction

With the rapid development of remote-sensing technology, more and more remotesensing images are employed for farmland monitoring, land use, target detection and so on in production and supporting living [1]. Due to the influence of complex atmospheric environments, most images cannot be directly used, and among these influencing factors is the presence of clouds. 70% of the world is often covered with clouds [2] leading to a compromised determination of the surface reflection information and significant impact on the analysis and application. Improved cloud-detection procedures are essential to service the requirements of a range of Earth applications. These methods can be divided into two classes: threshold-based and classification-based approaches

Methods

Results

Conclusion