Automatic Matching of Multimodal Remote Sensing Images via Learned Unstructured Road Feature

Automatic matching of multimodal remote sensing images remains a vital yet challenging task, particularly for remote sensing and computer vision applications. Most traditional methods mainly focus on key point detection and description of the original image, thus ignoring the deep semantic feature i...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 18; p. 4595
Main Authors Yu, Kun, Xu, Chengcheng, Ma, Jie, Fang, Bin, Ding, Junfeng, Xu, Xinghua, Bao, Xianqiang, Qiu, Shaohua
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2022
Subjects
Accuracy
Computer vision
Deep learning
Efficiency
Entropy
Feature extraction
feature matching
Global optimization
Image processing
Image segmentation
local binary entropy descriptor
Matching
Methods
multimodal image matching
Parameter estimation
Radiation
Remote sensing
Roads
Roads & highways
semantic road features
Semantics
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Summary:Automatic matching of multimodal remote sensing images remains a vital yet challenging task, particularly for remote sensing and computer vision applications. Most traditional methods mainly focus on key point detection and description of the original image, thus ignoring the deep semantic feature information such as semantic road features, with the result that the traditional method can not effectively resist nonlinear grayscale distortion, and has low matching efficiency and poor accuracy. Motivated by this, this paper proposes a novel automatic matching method named LURF via learned unstructured road features for the multimodal images. There are four main contributions in LURF. To begin with, the semantic road features were extracted from multimodal images based on segmentation model CRESIv2. Next, based on semantic road features, a stable and reliable intersection point detector has been proposed to detect unstructured key points. Moreover, a local entropy descriptor has been designed to describe key points with the local skeleton feature. Finally, a global optimization strategy is adopted to achieve the correct matching. The extensive experimental results demonstrate that the proposed LURF outperforms other state-of-the-art methods in terms of both accuracy and efficiency on different multimodal image data sets.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14184595