Article: An optimal dimension reduction-based feature selection and classification strategy for geospatial imagery Journal: International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP) 2019 Vol.6 No.2 pp.120 - 138 Abstract: Driven by the explosive growth on the available data nowadays and advancement of technologies, the strong need arises for utilising and maintaining the available data. However, while building an expert prediction system, the inconsistency present in the information system, incompleteness of available knowledge base, continuous natured attribute values and noise present in the system (especially in case of spatial image data handling), are prime factors which may degrade the process of classification with available traditional methods. Our proposed construction adopts an efficient strategy for classification. Here we explore the problem of classifying remote sensing satellite images. Image data pre-processing and its categorisation refers to the labelling of individual pixel object instances into one of a number of predefined categories. Although this is usually not a much intractable task for humans, it has proved to be an extremely difficult problem for machines. We performed experimental analysis for classification using <i>NWPU-RESISC45 dataset</i>. Experiment result shows the improvement in classification by adopting our proposed strategy over other significant state of the art. Inderscience Publishers - linking academia, business and industry through research

Title: An optimal dimension reduction-based feature selection and classification strategy for geospatial imagery

Authors: Ajeet Singh; Vikas Tiwari

Addresses: C.R. Rao Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus, Hyderabad 500046, India ' C.R. Rao Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad Campus, Hyderabad 500046, India

Abstract: Driven by the explosive growth on the available data nowadays and advancement of technologies, the strong need arises for utilising and maintaining the available data. However, while building an expert prediction system, the inconsistency present in the information system, incompleteness of available knowledge base, continuous natured attribute values and noise present in the system (especially in case of spatial image data handling), are prime factors which may degrade the process of classification with available traditional methods. Our proposed construction adopts an efficient strategy for classification. Here we explore the problem of classifying remote sensing satellite images. Image data pre-processing and its categorisation refers to the labelling of individual pixel object instances into one of a number of predefined categories. Although this is usually not a much intractable task for humans, it has proved to be an extremely difficult problem for machines. We performed experimental analysis for classification using NWPU-RESISC45 dataset. Experiment result shows the improvement in classification by adopting our proposed strategy over other significant state of the art.

Keywords: knowledge discovery; machine learning; classification; high-resolution satellite imagery.

DOI: 10.1504/IJKESDP.2019.102851

International Journal of Knowledge Engineering and Soft Data Paradigms, 2019 Vol.6 No.2, pp.120 - 138

Received: 27 Jul 2018
Accepted: 10 Feb 2019
Published online: 08 Oct 2019 *

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Title: An optimal dimension reduction-based feature selection and classification strategy for geospatial imagery

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