Automatic Bright Circular Type Oil Tank Detection Using Remote Sensing Images

Kushwaha, Naveen Kumar; Chaudhuri, Debasis; Singh, Manish

doi:10.14429/dsj.63.2737

Cited by 16 publications

(5 citation statements)

References 13 publications

(7 reference statements)

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“…(Zhu et al, 2012) developed a coarse-to-fine strategy in which the coarse level aimed at finding the image patches with oil tanks, and the fine level detected the circular regions. (Kushwaha et al, 2013) proposed a supervised strategy to detect bright oil tanks from satellite images, where the oil tanks were detected based on the analysis of their statistical and textural information.…”

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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“…Multi- There has been much research on oil tank detection based on high spatial resolution images. Kushwaha et al [6] proposed a knowledge-based strategy to detect bright oil tanks after morphological segmentation from high spatial resolution images. The Hough transform [7] and its related methods [8][9][10] have been widely used in oil tank detection [11], as they are efficient in finding circles.…”

Section: Introductionmentioning

confidence: 99%

Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images

Chen

Wang

et al. 2021

Remote Sensing

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Oil tank inventory is significant for the economy and the military, as it can be used to estimate oil reserves. Traditional oil tank detection methods mainly focus on the geometrical characteristics and spectral features of remotely sensed images based on feature engineering. The methods have a limited application capability when the distribution pattern of ground objects in the image changes and the imaging condition varies largely. Therefore, we propose an end-to-end deep convolution network Res2-Unet+, to detect oil tanks in a large-scale area. The Res2-Unet+ method replaces the typical convolution block in the encoder of the original Unet method using hierarchical residual learning branches. A hierarchical branch is used to decompose the feature map into a few sub-channel features. To evaluate the generalization and transferability of the proposed model, we use high spatial resolution images from three different sensors in different areas to train the oil tank detection model. Images from yet another sensor in another area are used to evaluate the trained model. Three more widely used methods, Unet, Segnet, and PSPNet, are trained and evaluated for the same dataset. The experiments prove the effectiveness, strong generalization, and transferability of the proposed Res2-Unet+ method.

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“…Few automatic oil tank detection algorithms are accessible in the literatures [8], [16]. Among them, template matching [9] and Hough transform [10]- [12] are common methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…Another technique based on image fusion and morphological operations is suggested by Qiang et al [15]. Kushwaha et al [16] proposed a method to extract bright oil tanks by morphological operation. But the method is unable to detect dark oil tanks.…”

Section: Introductionmentioning

confidence: 99%

Detection of Oil Tank from High Resolution Remote Sensing Images using Morphological and Statistical Tools

Chaudhuri¹,

Sharif²

2021

Machine Learning, IOT and Blockchain Technologies &Amp; Trends

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Oil tank is an important target and automatic detection of the target is an open research issue in satellite based high resolution imagery. This could be used for disaster screening, oil outflow, etc. A new methodology has been proposed for consistent and precise automatic oil tank detection from such panchromatic images. The proposed methodology uses both spatial and spectral properties domain knowledge regarding the character of targets in the sight. Multiple steps are required for detection of the target in the methodology – 1) enhancement technique using directional morphology, 2) multi-seed based clustering procedure using internal gray variance (IGV), 3) binarization and thinning operations, 4) circular shape detection by Hough transform, 5) MST based special relational grouping operation and 6) supervised minimum distance classifier for oil tank detection. IKONOS and Quickbird satellite images are used for testing the proposed algorithm. The outcomes show that the projected methodology in this paper is both precise and competent.

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Automatic Bright Circular Type Oil Tank Detection Using Remote Sensing Images

Cited by 16 publications

References 13 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

Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images

Detection of Oil Tank from High Resolution Remote Sensing Images using Morphological and Statistical Tools

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