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

Ok, Ali Ozgun; Baseski, Emre

doi:10.1109/lgrs.2015.2401600

Cited by 46 publications

(21 citation statements)

References 20 publications

(30 reference statements)

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“…However, their approach utilized many ad-hoc thresholds, and further problems associated with their approach are discussed in (Ok and Baseski, 2015), and an improved radial symmetry transform to detect circular objects from images were proposed. More recently, we proposed an orientation-based radial symmetry (Ok and Ozdarici-Ok, 2017) that is particularly designed to focus on above-ground circular objects from a DSM.…”

Section: Proposed Approachmentioning

confidence: 99%

Detection of Citrus Trees From Uav DSMS

Ok¹,

Ozdarici-Ok²

2017

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

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ABSTRACT:This paper presents an automated approach to detect citrus trees from digitals surface models (DSMs) as a single source. The DSMs in this study are generated from Unmanned Aerial Vehicles (UAVs), and the proposed approach first considers the symmetric nature of the citrus trees, and it computes the orientation-based radial symmetry in an efficient way. The approach also takes into account the local maxima (LM) information to verify the output of the radial symmetry. Our contributions in this study are twofold: (i) Such an integrated approach (symmetry + LM) has not been tested to detect (citrus) trees (in orchards), and (ii) the validity of such an integrated approach has not been experienced for an input, e.g. a single DSM. Experiments are performed on five test patches. The results reveal that our approach is capable of counting most of the citrus trees without manual intervention. Comparison to the stateof-the-art reveals that the proposed approach provides notable detection performance by providing the best balance between precision and recall measures.

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Section: Proposed Approachmentioning

confidence: 99%

Detection of Citrus Trees From Uav DSMS

Ok¹,

Ozdarici-Ok²

2017

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

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“…O. Ok et al [6] validation strategy is not given to understand whether detected circle belongs to a tank rooftop or not. Datasets are tested on panchromatic GeoEye-1 images (50 cm,11 bits).…”

Section: Literature Surveymentioning

confidence: 99%

Enhancement of an Algorithm for Oil Tank Detection in Satellite Images

Jivane¹,

Soundrapandiyan²

2017

IJIES

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Satellite imaging is the order of the day. Automatic oil tank detection is one of the important domains in satellite image processing which could be used for disaster monitoring, oil leakage, etc. It has great significance for the military and civilian application to detect oil tanks and locate oil depots automatically from remote satellite images. Automatic oil tank detection in satellite image remains a challenging problem. This paper addresses this problem using enhancement of algorithm for oil tank detection with SURF technique and SVM classifier approach. The proposed approach consists of four stages are pre-processing, segmentation, feature extraction and classification. Initially, the input image is pre-processed using threshold method. Secondly, SURF technique applied on the preprocessed image for the purpose of segment the oil tank. Then, well known features are extracted from the segmented image. Finally, to classify the oil tank and non oil tank support vector machine (SVM) is applied on the extracted feature. To test the proposed method the experiment is carried out the standard benchmark databases are SPOT-5 satellite images, QuickBird satellite images, GeoEye-1 satellite images and Google Earth images. The classification results obtained from the SVM classifier shows the efficiency of proposed method by high oil tank detection rate and low false alarm rate.

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“…In recent years, many oil tank detection methods have been proposed, which include the template matching method [1,2], geometric shape method [3][4][5][6][7], saliency detection method [8,9], and machine learning method [10][11][12]. The template matching method requires lots of calculations.…”

Section: Introductionmentioning

confidence: 99%

“…In terms of shape information, a new method of detecting oil tanks has appeared in recent years. Ok [7] proposes a detection method based on circular radial symmetry by calculating the boundary gradient direction and the center of the circle to obtain a very good detection result. But oil tanks cannot be detected completely when oil tanks are small or have shadows, three-dimensional structures, and low contrast.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Saliency Model with Color Markov Chain for Oil Tank Detection

Ziming¹,

Zhao²,

Shi³

et al. 2019

Remote Sensing

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Traditional oil tank detection methods often use geometric shape information. However, it is difficult to guarantee accurate detection under a variety of disturbance factors, especially various colors, scale differences, and the shadows caused by view angle and illumination. Therefore, we propose an unsupervised saliency model with Color Markov Chain (US-CMC) to deal with oil tank detection. To avoid the influence of shadows, we make use of the CIE Lab space to construct a Color Markov Chain and generate a bottom-up latent saliency map. Moreover, we build a circular feature map based on a radial symmetric circle, which makes true targets to be strengthened for a subjective detection task. Besides, we combine the latent saliency map with the circular feature map, which can effectively suppress other salient regions except for oil tanks. Extensive experimental results demonstrate that it outperforms 15 saliency models for remote sensing images (RSIs). Compared with conventional oil tank detection methods, US-CMC has achieved better results and is also more robust for view angle, shadow, and shape similarity problems.

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

Cited by 46 publications

References 20 publications

Detection of Citrus Trees From Uav DSMS

Detection of Citrus Trees From Uav DSMS

Enhancement of an Algorithm for Oil Tank Detection in Satellite Images

Unsupervised Saliency Model with Color Markov Chain for Oil Tank Detection

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