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

Ok, Ali Özgün; Baseski, Emre

doi:10.5194/isprsannals-ii-3-w4-149-2015

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Therefore, the documentation of the external geometries and the sites of historic buildings prior to their exhaustion or sabotage is * Corresponding author: nada.m.kadhim@gmail.com crucial, and could be performed with the assist of remotely sensed images. Satellite images could be useful for locating individual buildings, providing information about their shapes, virtual modelling the environment nearby (Ok , Baseski, 2015). Very High Resolution (VHR) satellite imageries are capable of providing high level of detail because of their ability to offer sub-metre resolution (Kadhim et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

Building Assessment Using Shadow Analysis for the Architectural Documentation

Kadhim

2019

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

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<p><strong>Abstract.</strong> One essential approach in preserving architectural heritage is the documentation of building geometries and their sites are thoroughly photographed of existing buildings. For instance, an automated detection of the locations and the extraction of geometry information of the buildings are intrinsic attributes of the architectural design of building interiors and exteriors in cultural heritage documentation and preservation. In this study, contributions of remotely sensed images to detect the sites and shapes of the buildings based on shadow analysis were evaluated. The aim of this paper is to develop an approach to assist in recording and documenting the external geometry of historic buildings and their real location supplying much ease and time-saving in protection projects of cultural heritage. A distinctive merit of the proposed approach is its ability to document the spatial distribution of the detecting buildings by extracting the graphic coordinates of their corners without any additional data. Our experiments demonstrate the efficiency of the proposed approach on two cases, including the architectural documentation of external edges, shape, size, site, orientation, and coordinates. Thus, our new approach offers a tool to document architectural and cultural heritage which can help in capturing and recording all the relevant data and information, both in visual and written format.</p>

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Section: Introductionmentioning

confidence: 99%

Building Assessment Using Shadow Analysis for the Architectural Documentation

Kadhim

2019

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

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“…O. Ok et al [3] presented the automated thresholding method. In this method, computation of gradient are applied on images and then applying 5-tap coefficient method for detecting oil depots.…”

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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