Journal Article

Object-Based Convolutional Neural Networks for Cloud and Snow Detection in High-Resolution Multispectral Imagers

Fan, Rongshuang, Chen, Yang, Tang, Luliang, Wang, Lei, Yao, Yunlong

2018

Overview

Cloud and snow detection is one of the most significant tasks for remote sensing image processing. However, it is a challenging task to distinguish between clouds and snow in high-resolution multispectral images due to their similar spectral distributions. The shortwave infrared band (SWIR, e.g., Sentinel-2A 1.55–1.75 µm band) is widely applied to the detection of snow and clouds. However, high-resolution multispectral images have a lack of SWIR, and such traditional methods are no longer practical. To solve this problem, a novel convolutional neural network (CNN) to classify cloud and snow on an object level is proposed in this paper. Specifically, a novel CNN structure capable of learning cloud and snow multiscale semantic features from high-resolution multispectral imagery is presented. In order to solve the shortcoming of “salt-and-pepper” in pixel level predictions, we extend a simple linear iterative clustering algorithm for segmenting high-resolution multispectral images and generating superpixels. Results demonstrated that the new proposed method can with better precision separate the cloud and snow in the high-resolution image, and results are more accurate and robust compared to the other methods.

• Publisher: MDPI AG
• Subject: Algorithms / Cloud computing / Clouds / Deep learning / image analysis / Image processing / multispectral imagery / Neural networks / Noise / prediction / Remote sensing / Semantics / snow