A Multiple Classifier System to improve mapping complex land covers: a case study of wetland classification using SAR data in Newfoundland, Canada

• Published in: International journal of remote sensing Vol. 39; no. 21; pp. 7370 - 7383
• Main Authors: Amani, Meisam, Salehi, Bahram, Mahdavi, Sahel, Brisco, Brian, Shehata, Mohamed
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 02.11.2018; Taylor & Francis Ltd
• Subjects: Accuracy; Algorithms; Canada; Case studies; Classification; Classifiers; Data; Imagery; Landscape; landscapes; Radar; Radar data; Remote sensing; SAR (radar); Satellite data; Satellite imagery; Satellites; Synthetic aperture radar; Wetlands; Newfoundland & Labrador Canada; Canada
• ISSN: 0143-1161; 1366-5901; 1366-5901
• DOI: 10.1080/01431161.2018.1468117

There are currently various classification algorithms, each with its own advantages and limitations. It is expected that fusing different classifiers in a way that the advantages of each are selected can boost the accuracy in the classification of complex land covers, such as wetlands, compared to using a single classifier. Classification of wetlands using remote-sensing methods is a challenging task because of considerable similarities between wetland classes. This fact is more important when utilizing synthetic aperture radar (SAR) data, which contain speckle noise. Consequently, discriminating wetland classes using only SAR data is generally not as accurate as using some other satellite data, such as optical imagery. In this study, a new Multiple Classifier System (MCS), which combines five different algorithms, was proposed to improve the classification accuracy of similar land covers. This system was then applied to classify wetlands in a study area in Newfoundland, Canada, using multi-source and multi-temporal SAR data. The results demonstrated that the proposed MCS was more accurate for the classification of wetlands in terms of both overall and class accuracies compared to applying one specific algorithm. Therefore, it is expected that the proposed system improves the classification accuracy of other complex landscapes.