Method for Rapidly Detecting Circlular-Object Clusters in Large Remote Sensing Images

Chen, Aijun

doi:10.1109/itcs.2009.115

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…(Weisheng et al, 2005) detected storage tanks from SPOT-5 pansharped images using an improved Hough Transform, and a correlation-based template matching. (Li, 2006) tested an approach based on segmentation and feature-based classification, while (Chen, 2009) employed a circle detection algorithm based on shape parameters and a region-growingbased clustering. (Han et al, 2011) utilized Hough transform to detect circles in QuickBird images and a graph-based search is developed to eliminate false detections.…”

Section: Previous Studiesmentioning

confidence: 99%

Automated Detection of Oil Depots From High Resolution Images: A New Perspective

Baseski

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents an original approach to identify oil depots from single high resolution aerial/satellite images in an automated manner. The new approach considers the symmetric nature of circular oil depots, and it computes the radial symmetry in a unique way. An automated thresholding method to focus on circular regions and a new measure to verify circles are proposed. Experiments are performed on six GeoEye-1 test images. Besides, we perform tests on 16 Google Earth images of an industrial test site acquired in a time series manner (between the years 1995 and 2012). The results reveal that our approach is capable of detecting circle objects in very different/difficult images. We computed an overall performance of 95.8% for the GeoEye-1 dataset. The time series investigation reveals that our approach is robust enough to locate oil depots in industrial environments under varying illumination and environmental conditions. The overall performance is computed as 89.4% for the Google Earth dataset, and this result secures the success of our approach compared to a state-of-the-art approach.

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Section: Previous Studiesmentioning

confidence: 99%

Automated Detection of Oil Depots From High Resolution Images: A New Perspective

Baseski

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Weisheng based template matching [10]. Li tested an approach based on segmentation and feature-based classification [11], while Chen employed a circle detection algorithm based on shape parameters and a region-growing-based clustering [12]. Han et al utilized Hough transform to detect circles in QuickBird images and a graph-based search to eliminate false detections [13].…”

Section: Introductionmentioning

confidence: 99%

Circular Oil Tank Detection From Panchromatic Satellite Images: A New Automated Approach

Baseski

2015

IEEE Geosci. Remote Sensing Lett.

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This letter introduces a new approach for the automated detection of circular oil tanks from single panchromatic satellite images. The new approach considers the symmetric nature of the circular oil depots, and it computes the radial symmetry in a unique way. We propose an automated thresholding method to focus on circular regions and a new measure, circle support ratio, to verify detected circles. Experiments are performed on GeoEye-1 test scenes, and the results reveal that the new approach is capable of detecting oil tanks with high success. The performance of our approach is also compared with leading techniques from the literature and has provided comparable or superior results.Index Terms-Circle detection, oil tanks, panchromatic satellite imagery, radial symmetry.

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Accurate and Robust Circular Object Detection Using Conditional Probability Searching

Shi

Wang

et al. 2012

2012 IEEE 12th International Conference on Computer and Information Technology

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Method for Rapidly Detecting Circlular-Object Clusters in Large Remote Sensing Images

Cited by 4 publications

References 5 publications

Automated Detection of Oil Depots From High Resolution Images: A New Perspective

Automated Detection of Oil Depots From High Resolution Images: A New Perspective

Circular Oil Tank Detection From Panchromatic Satellite Images: A New Automated Approach

Accurate and Robust Circular Object Detection Using Conditional Probability Searching

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