RoadCapsFPN: Capsule Feature Pyramid Network for Road Extraction From VHR Optical Remote Sensing Imagery
IEEE Transactions on Intelligent Transportation Systems(2022)
摘要
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.
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关键词
Road extraction,capsule feature pyramid network,capsule network,dense atrous convolution,residual multi-kernel pooling,remote sensing imagery
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