A Very High Resolution Satellite Imagery Classification Algorithm

Shedlovska, Y. I.; Hnatushenko, Volodymyr

doi:10.1109/elnano.2018.8477447

Cited by 9 publications

(3 citation statements)

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“…The best results for pixel-based and object-based techniques are shown in Figure (5). SVM classifier which gives the best classification results will be used in the refinement process.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…This method provided promising results, where neighborhood, contextual and geometrical features were used in addition to spectral features. An automatic classification algorithm based on the object-based method has been developed by Shedlovska and Hnatushenko [5]. The results showed that the method gives good results when the mean-shift method was applied in the segmentation step.…”

Section: Introductionmentioning

confidence: 99%

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Building Extraction From Very High-Resolution Satellite Images for Map Updating in Egypt

Shoaib

Mostafa

Abbas

2020

JES. Journal of Engineering Sciences

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Robust building detection from satellite images has been a subject of interest for several decades. Very High Resolution (VHR) satellite images support the efficient extraction of manmade objects. The main aim of this paper is to present an approach for building extraction from VHR satellite images for map updating in Egypt. To achieve this aim, a comparison of pixel and object-based classification techniques has been applied. Then, different refinement processes based on shadow, context, shape, and Digital Surface Model (DSM) data are carried out. Two study areas from the VHR satellite images for Assuit and Sohag cities are used. A comparison of the classification techniques shows that the Maximum Likelihood Classifier (MLC) for pixel-based technique and Support Vector Machine (SVM) for object-based technique give the highest overall accuracy results. Refinement based on shadow, context, shape, and DSM information improves the overall accuracy with an average of 18%. Thus, the building extraction results can contribute significantly to update maps in Egypt .

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“…The best results for pixel-based and object-based techniques are shown in Figure (5). SVM classifier which gives the best classification results will be used in the refinement process.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building Extraction From Very High-Resolution Satellite Images for Map Updating in Egypt

Shoaib

Mostafa

Abbas

2020

JES. Journal of Engineering Sciences

View full text Add to dashboard Cite

show abstract

“…The highest demand tasks are pattern recognition and image classification. These tasks are particularly important in the remote sensing area for satellite image processing [1,2]. Today a lot of aircrafts are able to provide high quality images of the Earth surface.…”

Section: Introductionmentioning

confidence: 99%