Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 249-255, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/249/2016/
doi:10.5194/isprs-archives-XLI-B7-249-2016
 
21 Jun 2016
REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE
Warinthorn Kiadtikornthaweeyot1 and Adrian R. L. Tatnall2 1Geo-Informatics and Space Technology Development Agency, 20 The Government Complex, Building 6th-7th Floor, Chaeng Wattana Road, Lak Si, Bangkok, 10210
2University of Southampton, University Road, Highfield, Southampton SO17 1BJ, UK
Keywords: Region of interest, Image segmentation, Histogram segmentation, Morphological, Satellite image Abstract. High resolution satellite imaging is considered as the outstanding applicant to extract the Earth’s surface information. Extraction of a feature of an image is very difficult due to having to find the appropriate image segmentation techniques and combine different methods to detect the Region of Interest (ROI) most effectively. This paper proposes techniques to classify objects in the satellite image by using image processing methods on high-resolution satellite images. The systems to identify the ROI focus on forests, urban and agriculture areas. The proposed system is based on histograms of the image to classify objects using thresholding. The thresholding is performed by considering the behaviour of the histogram mapping to a particular region in the satellite image. The proposed model is based on histogram segmentation and morphology techniques. There are five main steps supporting each other; Histogram classification, Histogram segmentation, Morphological dilation, Morphological fill image area and holes and ROI management. The methods to detect the ROI of the satellite images based on histogram classification have been studied, implemented and tested. The algorithm is be able to detect the area of forests, urban and agriculture separately. The image segmentation methods can detect the ROI and reduce the size of the original image by discarding the unnecessary parts.
Conference paper (PDF, 1752 KB)


Citation: Kiadtikornthaweeyot, W. and Tatnall, A. R. L.: REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 249-255, doi:10.5194/isprs-archives-XLI-B7-249-2016, 2016.

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