A Fuzzy Decision Tree for Processing Satellite Images and Landsat Data

• Published in: Procedia computer science Vol. 52; pp. 1192 - 1197
• Main Authors: Al-Obeidat, Feras, Al-Taani, Ahmad T., Belacel, Nabil, Feltrin, Leo, Banerjee, Neil
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2015
• Subjects: Decision Tree; Fuzzy Classification; Landsat; PROAFTN; Remote Sensing; Satellite Images; Remote Sensing; Decision Tree; PROAFTN; Fuzzy Classification; Satellite Images; Landsat
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2015.05.157

Satellite and airborne images, including Landsat, ASTER, and Hyperspectral data, are widely used in remote sensing and Geo- graphic Information Systems (GIS) to understand natural earth related processes, climate change, and anthropogenic activity. The nature of this type of data is usually multi or hyperspectral with individual spectral bands stored in raster file structures of large size and global coverage. The elevated number of bands (on the order of 200 to 250 bands) requires data processing algorithms capable of extracting information content, removing redundancy. Conventional statistical methods have been devised to reduce dimension- ality however they lack specific processing to handle data diversity. Hence, in this paper we propose a new data analytic technique to classify these complex multidimensional data cubes. Here, we use a well-known database consisting of multi-spectral values of pixels from satellite images, where the classification is associated with the central pixel in each neighborhood. The goal of our proposed approach is to predict this classification based on the given multi-spectral values. To solve this classification problem, we propose an improved decision tree (DT) algorithm based on a fuzzy approach. More particularly, we introduce a new hybrid classification algorithm that utilizes the conventional decision tree algorithm enhanced with the fuzzy approach. We propose an improved data classification algorithm that utilizes the best of a decision tree and multi-criteria classification. To investigate and evaluate the performance of our proposed method against other DT classifiers, a comparative and analytical study is conducted on well-known Landsat data.