Abstract
Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homogenous area by suitable parameters such as scale parameter, compactness, shape etc. Classification based on segments was done by a nearest neighbour classifier. In the pixel-based classification, the spectral angle mapper was used to classify the images. The user accuracy for each class using object based classification were 98.31% for waterbody, 92.31% for vegetation, 86.67% for bare soil and 90.57% for Built up while the user accuracy for the pixel based classification were 98.28% for waterbody, 84.06% for Vegetation 86.36% and 79.41% for Built up. These classification techniques were subjected to accuracy assessment and the overall accuracy of the Object based classification was 94.47%, while that of Pixel based classification yielded 86.64%. The result of classification and accuracy assessment show that the object-based approach gave more accurate and satisfying results
Highlights
According to the findings of [2], geospatial specialists have theorized the possibility of developing a fully automated classification procedure that would be an improvement over pixel-based procedures
Pixel-based and object-based image classification was performed on RapidEye satellite imagery with a 6.5m spatial resolution
Accuracy assessment results showed that object-based image classification obtained higher accuracy than pixel-based classification
Summary
According to the findings of [2], geospatial specialists have theorized the possibility of developing a fully automated classification procedure that would be an improvement over pixel-based procedures. Pixel-based procedures analyse the spectral properties of every pixel within an area of interest, without taking into account the spatial or contextual information related to Geoinformatics FCE CTU 15(2), 2016, doi:10.14311/gi.15.2.5. Makinde et al.: Object Based and Pixel Based Classification the pixel of interest. Since higher resolution satellite imagery is available, it could be used to produce very accurate classifications [13]. Researchers have generally observed that when pixel-based methods are applied to high-resolution satellite images a “salt and pepper” effect was produced that contributed to the inaccuracy of the classification [4]. Object-based classification seems to produce better results when applied to higher resolutions
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