Multi-scale hybrid saliency analysis for region of interest detection in very high resolution remote sensing images

Zhang, Libao; Qiu, Bingchang; Yu, Xianchuan; Xu, Jindong

doi:10.1016/j.imavis.2014.12.002

Cited by 26 publications

(9 citation statements)

References 20 publications

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“…Our method is based on the remark that image saliency should be detected from multiple scales, since bottom features (color, edge, blob, texture, etc.) at a special scale can only identify salient objects of the corresponding scale (Shapiro and Stockman 2001;Chalmond et al 2006;Zhang et al 2015).…”

Section: Workflow Of Saliency Detectionmentioning

confidence: 99%

“…A second variation was to look for blobs at multiple scales. Every object exists at a special scale in satellite images, and a single scale is not suitable to detect and describe saliency for all objects (Chalmond et al 2006;Zhang et al 2015). So multi-scale blobs were used to detect saliency map, as illustrated in Fig.…”

Section: Multi-scale Blobsmentioning

confidence: 99%

“…On the other hand, many applications, such as region/ object detection, are only focusing on some salient regions or objects in images (Itti et al 1998;Xu et al 2007). It is desired to extract regions and objects of interest before further image processing and analysis (Cui et al 2014;Zhang et al 2015).…”

Section: Introductionmentioning

confidence: 99%

“…And it is widely used for extraction of interesting regions and object recognition in natural images (Rosin 2009;Klein and Frintrop 2011) and satellite images (Li and Itti 2011;Chen et al 2014;Zhang et al 2015). Essentially, saliency at each location is determined by its dissimilarity from its neighborhood in terms of density, orientation, variance, junction, and edge density (Rosin 2009;Hu et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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Multi-Scale Blobs for Saliency Detection in Satellite Images

Yinqing

Luo

et al. 2015

J Indian Soc Remote Sens

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Saliency can be modeled as spatially localized and contrasted structures with higher blob density and higher blob evenness across scales in images. And it is likely to contain regions and objects of interest. So saliency detection is desired before further image processing and analysis. This paper presented an automatic and effective method for saliency detection in satellite images, based on multi-scale blob information. Firstly, multi-scale blob information were extracted from input images, to produce a blob map. Then, in the blob map, multi-level distance transform spread the blob information to the entire image, to generate a saliency map. Finally the saliency map was segmented to detect salient regions and to locate object centers. The experimental results illustrated its accuracy and stability for detecting salient regions (such as residential areas, parking lots and airplane docks) and for locating object centers in various satellite images.

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Section: Workflow Of Saliency Detectionmentioning

confidence: 99%

Section: Multi-scale Blobsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Scale Blobs for Saliency Detection in Satellite Images

Yinqing

Luo

et al. 2015

J Indian Soc Remote Sens

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“…With continuous development, edge detection algorithms have been used many areas thanks to the capability be able to simply use in short time and success rates increasing day to day. Areas of use; visual inspection part of automation systems (Hocenski, Vasilic & Hocenski, 2006), (Ersoy, 1987), identification of object boundaries by selecting the feature remotely sensed images (Ali & Clausi, 2001), ( Zhang, Qiu, Yu & Xu 2015), the use of facial recognition (Lee, Cham & Chen, 2002), ( Zhang, Tjondronegoro & Chandran 2014), feature extraction from images taken by satellite (Pirzada & Siddiqui, 2013), ( Mostert & Kriegler, 2005), extraction the characteristics of medical images (Bao & Sheng, 2013), etc.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Edge Detection Algorithms for Texture Analysis on Glass Production

Öztürk

Akdemïr

2015

Procedia - Social and Behavioral Sciences

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The use of technological innovations in production will increase the number of product and quality. With proposed method in this paper, it is aimed to improve the production process of glass which used almost every field. In this study, some of the popular edge detection algorithms (Roberts, Prewitt, Sobel, LoG and Canny) are used for the texture analysis process. It is aimed to determine glass surface defect with the applied of mentioned edge detection operators to same image. The results obtained from application are compared with the reference image and texture analysis performance of edge detection algorithms are evaluated. In this study the used material is glass and it is aimed to determine the glass surface defect such as scratch, crack and bubble with the use of edge detection operators. Glass is a difficult material to examine with cameras because glass has reflection and the transparency features. So, some improvement are applied in the image before edge detection algorithms are applied.Performed controlled experiments showed that LoG edge detection algorithm is better than other edge detection algorithms in determining texture analysis.

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