Accelerated Shadow Detection and Removal Method

Richter, Edward; Raettig, Ryan M; Mack, Joshua; Valancius, Spencer; Unal, Burak; Akoglu, Ali

doi:10.1109/aiccsa47632.2019.9035242

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The remote sensing images, however, are prone to aerosol and sensor noise, which further complicates related shadow research. This is the reason why commonly used algorithms are absent from shadow detection and compensation for remote sensing images with a complex background and multiple shadows [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Zhou

Sun

et al. 2021

Remote Sensing

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Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

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Section: Introductionmentioning

confidence: 99%

Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Zhou

Sun

et al. 2021

Remote Sensing

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Accelerated point set registration method

Raettig

Anderson

Nykl

et al. 2023

Journal of Defense Modeling & Simulation

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In computer vision and robotics, point set registration is a fundamental issue used to estimate the relative position and orientation (pose) of an object in an environment. In a rapidly changing scene, this method must be executed frequently and in a timely manner, or the pose estimation becomes outdated. The point registration method is a computational bottleneck of a vision-processing pipeline. For this reason, this paper focuses on speeding up a widely used point registration method, the iterative closest point (ICP) algorithm. In addition, the ICP algorithm is transformed into a massively parallel algorithm and mapped onto a vector processor to realize a speedup of approximately an order of magnitude. Finally, we provide algorithmic and run-time analysis.

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Accelerated Shadow Detection and Removal Method

Cited by 2 publications

References 7 publications

Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Accelerated point set registration method

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