Road detection plays an important role in a wide range of applications. However, due to size variations, spectral diversities, occlusions, and complex scenarios, it is still challenging to accurately extract roads from very-high resolution (VHR) optical remote sensing images. This paper proposes a capsule feature pyramid network for extracting road networks from VHR optical images, termed as RoadCapsFPN. By designing a capsule feature pyramid network, the RoadCapsFPN extracts and integrates multiscale capsule features to recover a high-resolution and semantically strong road feature representation. Next, we also design a contextual feature module, including dense atrous convolution (DAC) and residual multi-kernel pooling (RMP) units, to further exploit rich contextual properties of the roads at a high-resolution perspective. Benefitting from the multiscale feature abstraction and context augmentation, our RoadCapsFPN shows impressing results in processing variedly-sized and diversely-spectral roads in complex environments. Two testing datasets, Google and Massichusate Roads Datasets, are used for evaluating the proposed RoadCapsFPN via four testing indicators - precision, recall, intersection-over-union (IoU), and $F_{1}$ -score. Comparative studies also confirm the superior performance of the RoadCapsFPN in accurately extracting road networks.
RoadCapsFPN: Capsule Feature Pyramid Network for Road Extraction From VHR Optical Remote Sensing Imagery
IEEE Transactions on Intelligent Transportation Systems ; 23 , 8 ; 11041-11051
01.08.2022
2745980 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Road Recognition From Remote Sensing Imagery Using Incremental Learning
Online Contents | 2017
|British Library Conference Proceedings | 2013
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