Comparison of a new algorithm with the supervised classifications

Emrahoğlu, Nuri; Yeğingil, I.; Peştemalci, V.; Şenkal, Ozan; Kandirmaz, H. M.

doi:10.1080/01431160210145597

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…We use the overall accuracy, Kappa statistic and a relative measure-total normalized probability of misclassification (TNPM) [10] to compare the results of the three classifiers. Comparison of results can be seen in TABLE III.…”

Section: B Results and Comparisonmentioning

confidence: 99%

Classification of Landsat ETM+ Data Using a Bayesian Multi-net Classifier

Yun¹,

Li³

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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The purpose of this study is to investigate the applicability of Bayesian Multi-net Classifier (BMC) to classify a Landsat ETM+ image of Beijing acquired on May 1, 2003. BMC is based on Bayesian Network (BN), which is a graphical model encoding probabilistic relationships among variables of interest. Different from the BNC that has a mere network, a BMC has as many local Bayesian Networks as the predefined classes, which means that the probabilistic relationships among the features can be different for different classes. Classification is done by computing the probability of the class given the particular instance of the features, and then predicting the class with the highest posterior probability. Based on the overall accuracy, Kappa statistic and a relative measure-total normalized probability of misclassification (TNPM), classification results of BMC were compared with those of Maximum Likelihood Classifier (MLC) and Bayesian Network Classifier (BNC) in the case study. It is concluded from the comparison results that BMC is an effective approach in this case study.

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Section: B Results and Comparisonmentioning

confidence: 99%

Classification of Landsat ETM+ Data Using a Bayesian Multi-net Classifier

Yun¹,

Li³

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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“…Supervised classification approach with Maximum Likelihood Classifier (MLC) algorithms was employed to generate the three land cover maps using the band combinations of 4, 3, 2 (Red, Green and Blue bands) in the Thematic Mapper and ETM+7; and band 3, 4, 5 of the Landsat 8 imagery. Supervised classification of imagery through MLC is the most widely accepted and standard algorithm classifier for assessing land cover change (Emrahoğlu et al, 2003), as it examines both variance and covariance of spectral classes (Lillesand et al, 2004). Using this technique, training samples were selected to generate the land cover classes which are typical representatives of the prevailing land cover types of the study area.…”

Section: Data Processing and Techniquesmentioning

confidence: 99%

Observing land-use/land-cover dynamics in semi-arid environment: Evidence from Damaturu Town and its surrounding lands, Yobe State, Nigeria

Babagana-Kyari¹,

Boso²

2020

SJES

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The fragile Sudano-Sahelian ecological zone of Nigeria has been classified as a hotspot of land cover change (LCC) that has been suffering from serious anthropogenic and biophysical stresses. Damaturu, being the fastest growing town situated in the region happened to be a victim of this negative development. The purpose of this study is to remotely observe and assess the prevailing land-use/land-cover (LULC) dynamics of Damaturu town and its delicate surrounding lands from the year 1987-2017 study periods. To achieve this, a supervised image classification technique with Maximum Likelihood Classifier (MLC) algorithm was used in ERDAS Imagine version 15 software to classify the three epochs multi-temporal and multi-spectral Landsat imageries (TM 1987, ETM+7 2000 and OLI 2017). The classified LULC maps and their resulting statistics were then used to assess the spatio-temporal aspects of the observed changes by placing the results within the wider context of previous related literature and evidences. Findings revealed that the built-up area has been expanding since 1987 with an annual change rate of 4.5% between 1987-2000, and 5.3% during 2000-2017 respectively. The growth of the town is being accompanied by massive farmlands expansion and vegetal cover (trees and shrubs) lost making the surrounding arable lands seriously disturbed. Thus, if the observed trends continue, the entire studied region will be subjected to severe environmental hazard such as desertification. Overall, the study provides valuable information required for sustainable environmental management.

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“…Franklin and Wulder (2002) have regarded parametric classifi ers such as MLC as robust and well-behaved and capable of providing optimal or near-optimal decisions on covertype classes. Emrahoglu et al (2003), while conceding that MLC is the most popular statistical algorithm and widely accepted standard approach, compared its performance with a new algorithm devised by selecting features from both condensed nearest neighbour (CNN) and a standard normal distribution. The authors refer to this technique as the selected pixel classifi cation (SPC) method as opposed to nearest neighbour method where whole pixels are used for the purpose of classifying an image.…”

Section: Maximum Likelihood Classifi Cationmentioning

confidence: 99%

Topographic normalization of satellite imagery for image classification in northeast India

Reddy¹,

Blah

2009

Progress in Physical Geography: Earth and Environment

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IRS-LISS-III satellite imagery covering Nongkhyllem Wildlife Sanctuary area located within the Ri-Bhoi District of Meghalaya State, northeast India, was used for analysis of the landcover pattern and vegetation types occurring there. A maximum likelihood classifier was used to generate a supervised classification into land-cover types and the vegetation types within the forested area. The preparation of training data sets used thematic maps of the area, and knowledge accruing from extensive personal field visits. Sample field plots were located at 30 different places in the Sanctuary for classification accuracy assessment. The Normalized Difference Vegetation Index (NDVI) was also computed from LISS-III satellite imagery. A digital elevation model (DEM) of the Sanctuary was generated using a GIS. The DEM was used to test the hypothesis that its joint use with the satellite data would increase classification accuracy. This proved to be the case. Bivariate correlation analysis was performed between spectral and DEM variables to cross-check the results. In the example used, that of the rugged terrain in mountainous parts of northeast India, such integration of satellite land-cover data and DEM information appears to be a necessity in improved land-cover mapping for resource planning and utilization.

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Comparison of a new algorithm with the supervised classifications

Cited by 9 publications

References 9 publications

Classification of Landsat ETM+ Data Using a Bayesian Multi-net Classifier

Classification of Landsat ETM+ Data Using a Bayesian Multi-net Classifier

Observing land-use/land-cover dynamics in semi-arid environment: Evidence from Damaturu Town and its surrounding lands, Yobe State, Nigeria

Topographic normalization of satellite imagery for image classification in northeast India

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