激光与光电子学进展, 2019, 56 (16): 162804, 网络出版: 2019-08-05   

基于改进M型卷积网络的RGB彩色遥感图像云检测 下载: 1087次

Cloud Detection of RGB Color Remote Sensing Images Based on Improved M-Net
作者单位
1 南京信息工程大学江苏省大气环境与装备技术协同创新中心, 江苏 南京 210044
2 南京信息工程大学电子与信息工程学院, 江苏 南京 210044
3 中国气象局中国遥感卫星辐射测量和定标重点开放实验室 国家卫星气象中心, 北京 100081
摘要
RGB彩色图像中云没有明显的颜色分布与纹理模式,导致云检测易产生误检且细节丢失严重。针对这一问题,提出一种改进的M型卷积网络(RM-Net)模型,实现端到端的像素级语义分割。对原始数据集进行增强,并标注对应的像素级标签。利用空洞空间金字塔池化,在不丢失信息的前提下提取图像多尺度特征,并结合残差单元使网络不易出现退化。利用编码器模块与左路径提取图像全局上下文信息,利用解码器模块与右路径恢复图像空间分辨率,根据融合后的特征判别每个像元的类别概率,将其输入分类器进行像素级的云和非云分割。对Landsat8和高分一号WFV RGB彩色图像进行训练和测试,实验结果表明本文方法在不同条件下能很好地检测云边缘细节,并取得较高精度的云阴影检测,由此证明本文方法具有较好的泛化性与稳健性。
Abstract
Cloud detection is prone to error and considerable loss of details because clouds do not have obvious color distribution and texture pattern in RGB color images. Therefore, this study proposes an improved M-Net model called the RM-Net model to achieve end-to-end pixel-level semantic segmentation. An original dataset is enhanced and a corresponding pixel-level label is marked. Multi-scale image features are extracted without losing data via atrous spatial pyramid pooling, and residual units are combined to make the network resistant to degradation. Global context informations of the images are extracted using the encoder module and the left path. The spatial resolutions of the images are restored using the decoder module and the right path. Each pixel's category probability is determined based on fused features, and pixel-level cloud and non-cloud segmentation are performed using the input classifier. When training and testing Landsat8 and GaoFen-1 WFV RGB color images, experimental results show that the proposed method can well detect cloud edge details under various conditions and achieve high-precision cloud shadow detection, thus demonstrating that the proposed method has high generalization and robustness.

胡敬锋, 张秀再, 杨昌军. 基于改进M型卷积网络的RGB彩色遥感图像云检测[J]. 激光与光电子学进展, 2019, 56(16): 162804. Jingfeng Hu, Xiuzai Zhang, Changjun Yang. Cloud Detection of RGB Color Remote Sensing Images Based on Improved M-Net[J]. Laser & Optoelectronics Progress, 2019, 56(16): 162804.

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