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.
Highlights
The satellite image is the most important tool to extract the Earth’s surface information
A real scene observed from a satellite image contains a variety of features, textures and shadows and it can be very complex to detect the region of interest (ROI)
This paper proposes the use of histogram segmentation techniques to classify the object in the optical satellite image
Summary
The satellite image is the most important tool to extract the Earth’s surface information. Satellite imaging is able to compute estimates of agricultural areas due to being able to cover a wide region (Soontranon et al, 2015). The rice field area must be classified from other plants, in this paper the technique to classify the object in the optical satellite had been studied and tested. A real scene observed from a satellite image contains a variety of features, textures and shadows and it can be very complex to detect the region of interest (ROI). Image segmentation has been developed for extracting different features or textures inside an image. This can be performed a number of different ways using the image properties. This paper proposes the use of histogram segmentation techniques to classify the object in the optical satellite image. The final section gives a conclusion and identifies future work
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