The Effect of Shadow Area on SGM Algorithm and Disparity Map
Refinement From High Resolution Satellite Stereo Images

Tatar, Nurollah; Saadatseresht, M.; Arefi, Hossein

doi:10.5194/isprs-archives-xlii-4-w4-281-2017

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Stereo matching is the core of the binocular stereo vision system, which matches corresponding pixels in two or more viewpoints and obtains a disparity image. Semi-Global Block Matching (SGBM) [32][33][34] is more efficient in stereo matching than a local matching algorithm. It is widely used, but much more complicated.…”

Section: Stereo Matchingmentioning

confidence: 99%

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

et al. 2022

Forests

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Forest fires have the characteristics of strong unpredictability and extreme destruction. Hence, it is difficult to carry out effective prevention and control. Once the fire spreads, devastating damage will be caused to natural resources and the ecological environment. In order to detect early forest fires in real-time and provide firefighting assistance, we propose a vision-based detection and spatial localization scheme and develop a system carried on the unmanned aerial vehicle (UAV) with an OAK-D camera. During the high incidence of forest fires, UAVs equipped with our system are deployed to patrol the forest. Our scheme includes two key aspects. First, the lightweight model, NanoDet, is applied as a detector to identify and locate fires in the vision field. Techniques such as the cosine learning rate strategy and data augmentations are employed to further enhance mean average precision (mAP). After capturing 2D images with fires from the detector, the binocular stereo vision is applied to calculate the depth map, where the HSV-Mask filter and non-zero mean method are proposed to eliminate the interference values when calculating the depth of the fire area. Second, to get the latitude, longitude, and altitude (LLA) coordinates of the fire area, coordinate frame conversion is used along with data from the GPS module and inertial measurement unit (IMU) module. As a result, we experiment with simulated fire in a forest area to test the effectiveness of this system. The results show that 89.34% of the suspicious frames with flame targets are detected and the localization error of latitude and longitude is in the order of 10−5 degrees; this demonstrates that the system meets our precision requirements and is sufficient for forest fire inspection.

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Section: Stereo Matchingmentioning

confidence: 99%

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

et al. 2022

Forests

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Evaluation of Selected Cost Aggregation Methods on High Resolution Satellite Stereo Images

Tatar

Arefi

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Dense stereo processing requires a critical step that called cost aggregation or cost optimization. Most of the cost aggregation methods are evaluated on close range stereo images from Middlebury or KITTI datasets. While the effect of cost aggregation on high resolution satellite stereo processing has not yet been sufficiently evaluated. In this paper, three typical cost aggregation methods together with another approach which is a combination of these methods are evaluated on high resolution satellite stereo images and then are compared with LiDAR ground truth. These methods including Semi-Global Matching (SGM), Guided Filtering (GF), iterative GF (IGF), and SGM followed by GF (SGM-GF) with Census and Zero Normalized Cross Correlation (ZNCC) cost functions. Although the Census cost function has a good performance on the border of the objects and low blurring effects, the results of both cost functions, i.e. Census and ZNCC, have same treatment on all stereo methods. Also, in order to make an impartial assessment, for all stereo methods, we do not perform any disparity map refinement. The bad-pixel criteria with an absolute difference height error greater than 2 meters for SGM, GF, IGF, and SGM-GF methods is 36.7%, 34.8%, 33.8%, and 28.6% respectively. Also, the Normalized Median Absolute Difference (NMAD) error for SGM, GF, IGF, and SGM-GF is 1.29, 1.15, 1.06, and 0.94 meters, respectively. Overall, the experimental results on WV III stereo images demonstrate that the SGM method has lower accuracy and SGM-GF method is accurate than other methods.

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The Effect of Shadow Area on SGM Algorithm and Disparity Map Refinement From High Resolution Satellite Stereo Images

Cited by 2 publications

References 11 publications

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV

Evaluation of Selected Cost Aggregation Methods on High Resolution Satellite Stereo Images

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