Dr. Bolanle T. Abe Profile

Dr. Bolanle T. Abe

at Tshwane University of Technology

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Proceedings Article | 24 December 2013 Paper

Ensemble classifier using GRG algorithm for land cover classification

Bolanle Abe, J. Jordaan, Tshilidzi Marwala

Proceedings Volume 9067, 90670O (2013) https://doi.org/10.1117/12.2050094

KEYWORDS: Image processing, Hyperspectral imaging, Remote sensing, Sensors, Image sensors, Satellite imaging, Earth observing sensors, Satellites, Reflectivity, Error analysis

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Image processing is of great value because it enables satellite images to be translated into useful information. The preprocessing of remotely sensed images before features extraction is important to remove noise and improve the ability to interpret image data more accurately. All images should appear as if they were acquired from the same sensor at the end of image preprocessing. A major challenge associated with hyperspectral imagery in remote sensing analysis is the mixed pixels which are due to huge dimension nature of the data. This study makes a positive contribution to the problem of land cover classification by exploring Generalized Reduced Gradient (GRG) algorithm on hyperspectral datasets by using Washington DC mall and Indiana pines test site of Northwestern Indiana, USA as study sites. The algorithm was used to estimate the fractional abundance in the datasets for land cover classification. Ensemble classifiers such as random forest, bagging and support vector machines were implemented in Waikato Environment for knowledge Analysis (WEKA) to carry out the classification procedures. Experimental results show that random forest ensemble outperformed the other ensemble methods. The comparison of the classifiers is crucial for a decision maker to consider compromises in accuracy technique against complexity technique.

Proceedings Article | 24 December 2013 Paper

Hyperspectral image classification based on NMF Features Selection Method

Bolanle Abe, J. Jordaan

Proceedings Volume 9067, 90670N (2013) https://doi.org/10.1117/12.2050072

KEYWORDS: Hyperspectral imaging, Feature selection, Image classification, Neural networks, Remote sensing, Dimension reduction, Data mining, Image quality, Classification systems, Sensors

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Hyperspectral instruments are capable of collecting hundreds of images corresponding to wavelength channels for the same area on the earth surface. Due to the huge number of features (bands) in hyperspectral imagery, land cover classification procedures are computationally expensive and pose a problem known as the curse of dimensionality. In addition, higher correlation among contiguous bands increases the redundancy within the bands. Hence, dimension reduction of hyperspectral data is very crucial so as to obtain good classification accuracy results. This paper presents a new feature selection technique. Non-negative Matrix Factorization (NMF) algorithm is proposed to obtain reduced relevant features in the input domain of each class label. This aimed to reduce classification error and dimensionality of classification challenges. Indiana pines of the Northwest Indiana dataset is used to evaluate the performance of the proposed method through experiments of features selection and classification. The Waikato Environment for Knowledge Analysis (WEKA) data mining framework is selected as a tool to implement the classification using Support Vector Machines and Neural Network. The selected features subsets are subjected to land cover classification to investigate the performance of the classifiers and how the features size affects classification accuracy. Results obtained shows that performances of the classifiers are significant. The study makes a positive contribution to the problems of hyperspectral imagery by exploring NMF, SVMs and NN to improve classification accuracy. The performances of the classifiers are valuable for decision maker to consider tradeoffs in method accuracy versus method complexity.

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