DOI number:10.1117/1.jrs.13.036505

Journal:JOURNAL OF APPLIED REMOTE SENSING

Key Words:remote sensing,haze removal,deep learning,haze removal fully convolutional network

Abstract:In many remote sensing (RS) applications, haze greatly affects the quality of optical RS images, but we do not always have the conditions to acquire multiple images in the same area for haze removal tasks. Therefore, the research on haze removal from a single RS image is necessary. Previous haze-removal methods introduce various prior knowledge to solve this problem, and thus, the quality of these methods largely depends on the reliability and validity of prior knowledge, which brings various limitations. We propose and validate a deep-learning-based model for haze removal, named haze removal fully convolutional network, to estimate transmission maps and generate corresponding haze-removed images via an atmospheric scattering model. Moreover, we propose an approximate method to produce hazy-and-clear image pairs as a dataset for training and validation. Experiments using this dataset demonstrated that the proposed model achieved the desired results in both visual effect and quantitative measurement. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

Co-author:Puyun Liao,Guanzhou Chen,Kun Zhu,Qing Wang,Xiaoliang Tan

Indexed by:Journal paper

Correspondence Author:Xiaodong Zhang

Document Type:J

Volume:13

Issue:3

Translation or Not:no

Date of Publication:2019-08-02

Included Journals:SCI