REGION OF INTEREST DETECTION BASED ON HISTOGRAM SEGMENTATION FOR SATELLITE IMAGE
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 I...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/249/2016/isprs-archives-XLI-B7-249-2016.pdf |
| Summary: | 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. |
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| ISSN: | 1682-1750 2194-9034 |