Comparison of Pixel-Based and Object-Oriented Classification Using Ikonos Imagery for Automatic Building ExtractionSafranbolu Testfield (original) (raw)
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Abstract: Automated features extraction techniques have gone a long way to ameliorate pains on imageprocessing and information extraction from remotely sensed image; this has opened many doors for manyapplications. However, pixel based image classification and feature extraction of Nigerian urban areas are stillvery difficult due to the spectral similarity of many of the urban features such as roads, bare ground, rockoutcrop, built-up areas and many more. This is as a result of the nature of the roads (untarred) and otherfeatures like built-up areas with large area coverage of bare ground rather than well landscaped with grassgardens. This makes high classification accuracy of urban feature extraction very difficult to achieve. Theperformance of urban feature extraction from LANDSAT imagery of Abuja, Nigeria with eCognition isoverwhelming. This research compared the result of object oriented image classification (i.e. eCognition) andother traditional (i.e. Erdas Imagine) methods of image classification vis-à-vis urban feature extraction frommedium resolution remotely sensed image of Abuja Nigeria which has a very highly similar spectral reflectancevalue. The result shows overall accuracy of 94.9% as against the overall accuracy of 89% in using thetraditional method in Erdas Imagine. While the ever difficult and very confused class differentiation in imageclassification in Nigerian urban areas between built-up and bare ground was clearly separated with high levelof accuracy Keyword: Pixel Based Classification, Object-Oriented Classification, Automatic Feature Extraction, eCognition.
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In this study, land cover types in Dead Sea area were analyzed on the basis of the classification results acquired using the pixel based and object-oriented image analysis approaches. A subset of Landsat TM satellite data was used for a comparison between pixel-based and object-based classification approaches. Ground truth data were collected from the available maps, aerial photographs, and personal knowledge. In pixel-based supervised classification, the spectral information for each pixel is utilized as the basis of categorization, the result shows that there are some small areas of anomalous pixels (salt and pepper) representing the noise in the data within the same class. On the other hand, object-oriented image analysis was evaluated based on object characteristics. This approach classifies not single pixels but groups of pixels that represent already existing objects in the field represent the n dimensional feature space for the classification. The result indicated an overall ...
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In this research, two techniques of pixel-based and object-based image analysis were investigated and compared for providing land use map in arid basin of Mokhtaran, Birjand. Using Landsat satellite imagery in 2015, the classification of land use was performed with three object-based algorithms of supervised fuzzy-maximum likelihood, maximum likelihood, and K-nearest neighbor. Nine combinations were examined in terms of scale level (SL10, SL30, and SL50) and the nearest neighborhood (NN3, NN5, and NN7) in an object-based classification. Ultimately, the validity was evaluated through the usage of two disagreement components including allocation disagreement and quantity disagreement. Results of maximum likelihood classification showed higher overall inaccuracycompared to images categorized based on fuzzy-maximum likelihood and object-based nearest neighbor algorithms. The SL30-NN3 object-based classifier decreased the quantity disagreement by 290% compared to the maximum likelihood a...
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 areas by suitable parameters such as a scale parameter, compact-ness, 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 water body, 92.31% for vegetation, 86.67% for bare soil and 90.57% for built up areas while the user accuracy for the pixel-based classification were 98.28% for water body, 84.06% for vegetation 86.36% and 79.41% for built up areas. 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 results of classification and its accuracy assessment show that the object-based approach gave more accurate and satisfying results.
Extraction of built-up areas from Landsat imagery using the object-oriented classification method
In the last years, optical satellite imagery with different spatial and spectral resolutions has become an important source for the extraction of built-up areas. The medium resolution Landsat images are available for the time period since 1972, and they successfully have been used for a multi-temporal analysis of urban areas. The time-series Landsat images are helpful to extract the urban extent area between the image acquisition years and to determine the urban growth by use of the common approach of the change detection analysis. This paper presents results of object-oriented classification based on Landsat imageries conducted using eCognition Developer software. The extraction of built-up area task is focused to analysis of urban extent of the Karakol city in Kyrgyzstan as the case study area. The overall accuracy was 88 %, 91 % and 94 % for the classified images in 1977, 1990 and 2011, respectively.
Recent Advances in Environmental Science from the Euro-Mediterranean and Surrounding Regions (2nd Edition), 2021
The monitoring of the earth surface and the atmosphere on a global, regional, and even local scale has become very accessible, thanks to the new and powerful techniques of remote sensing. Thus, it has become easy to access important coverage, mapping, and classification of the land covering features, including soil, vegetation, as well as water. Monitoring the coastal environment using remote sensing and GIS techniques has been undertaken in this study, with a particular focus on the comparison between the classical and object-oriented image classifications of remote sensing imagery in coastal areas. In fact, the investigation was based on the testing of a coastal zone image classification, pixel-based image classifiers such as SVM classifier and an object-oriented image classifier. The method was later compared using a Pleiades image. The use of reference data sets that were taken from high-resolution satellite images, aerial photographs, and field investigation was considered as an effective way to assess the accuracy of this method. Overall accuracy of 88% with a kappa coefficient of 0.74, compared with 79% (0.71) that was concluded from the conventional pixel-based method, was the result of this object-oriented method.
IOP Conference Series: Earth and Environmental Science, 2016
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This study focuses on the comparison between three image classifications of remote sensing imagery to estimate change detection in urban area (Tabriz, Iran) by Post Classification Comparison (PCC) technique. In order to investigate an appropriate method for extracting changes, pixel-based image classifiers such as: Maximum likelihood Classifier (MLC), Neural Network Classification (NN) and an Object-Oriented (OO) image Classifier were tasted and compered by using Landsat TM and ETM+ image respectively belong to 1990 and 2010. A priori defined five land cover classes in classification scheme were built-up, vegetated area, bare areas, water bodies and roads.
A multi-resolution object-based image analysis (OBIA) is used on a massive construction site using Landsat-8 operational land imager (OLI) data to discriminate the structural displacement of the deposits of soil properties, bog development, swampy conditions, marshy areas and acclamation shallow water as a result of clearing, removal and excavation processes was investigated. OLI have land-use and land-cover sensitive spectral band 1 (0.43-0.45 micrometer), spatial resolution of 30m and spectral band 2, 3 and 4 (0.45-0.51, 0.53-0.59 and 0.64-0.67 micrometer), its spatial resolution of 30 meter facilitates the detection and identification of the targets spectral differences. The results of this study show that, classification of remotely sensed data gives valuable information about the construction site where activities in terms of site clearing, removal of top soil and bulk excavation can be detected and identified. The study shows that OBIA images increases the quality of the terra...
Comparing pixel based and object based approaches in land use classification in mountainous areas
Remote sensing imagery needs to be converted into tangible information which can be utilized in conjunction with other data sets, often within widely used Geospatial Information Systems (GIS). Remote sensing data help in mapping land resources, especially in mountainous areas where accessibility is limited. Classification of remote sensing data in mountainous terrain is problematic because of variations in the sun illumination angle. Traditional approaches have many problems in these conditions. In object based approach can utilized GIS tools for improvement of classification results. In the present work we used pixel based and object based approaches that in both we imported GIS concepts and ancillary data for refining of classification results. The results showed that object based approach have higher accuracy than pixel based approach.