Comparison between Possibilistic c-Means (PCM) and Artificial Neural Network (ANN) Classification Algorithms in Land use/ Land cover Classification

• Published in: International journal of knowledge content development & technology Vol. 7; no. 1; pp. 57 - 78
• Main Authors: Ganbold, Ganchimeg, Chasia, Stanley
• Format: Journal Article
• Language: English
• Published: Chungju 건국대학교 지식콘텐츠연구소 01.03.2017; Research of Knowledge Content Development & Technology; Research Institute for Knowledge Content Development & Technology; 지식콘텐츠연구소
• Subjects: Accuracy; Algorithms; Artificial Neural Network; Artificial neural networks; Classification; Emergency preparedness; Error analysis; Eucalyptus; Globalization; Grasslands; Image classification; Infrared signatures; Input output; Land cover; Land use; Landsat satellites; Neural networks; Planning; Possibilistic c-Means; Remote sensing; Satellite imagery; Spectra; Spectral resolution; Sustainable development; Vegetation mapping; Wheat; 문헌정보학; Kenya; Fuzzy c-Means; Artificial Neural Network; Possibilistic c-Means
• ISSN: 2234-0068; 2287-187X
• DOI: 10.5865/IJKCT.2017.7.1.057

There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.