Spectral indices based object oriented classification for change detection using satellite data

Abhishek Bhatt (), S. K. Ghosh and Anil Kumar
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Abhishek Bhatt: Indian Institute of Technology
S. K. Ghosh: Indian Institute of Technology
Anil Kumar: Indian Institute of Remote Sensing

International Journal of System Assurance Engineering and Management, 2018, vol. 9, issue 1, No 6, 33-42

Abstract: Abstract Change detection, using remotely sensed data can be utilized in a diversified way such as, land use and cover analysis, forest or vegetation assessment, and flood monitoring. The aim of this study is to develop a methodology for change detection in highly urbanized areas, using time-series satellite imagery. This paper analyzes the effectiveness of the object oriented classification over unsupervised algorithms such as k-means for the purpose of change detection. The study area selected is, National Capital Territory of Delhi, which is a good representative of the urban agglomeration conditions in the Asian region. A time series of Landsat 5 (TM) and Landsat 8 (OLI) imageries for the duration of 1993–2014, have been acquired in order to represent the wide range of pattern variation. This study uses three spectral indices namely, Normalized Difference Built-up Index to characterize built-up area, Modified Normalized Difference Water Index to signify open water and Modified Soil Adjusted Vegetation Index to symbolize green vegetation. Further, this study employs object-based environment that separates similar pixels spatially and spectrally at different scales and assign information class to segmented objects. Results recommends the use of OBIA as a semi-automated tool for classification of remotely sensed satellite data. Moreover, using OBIA along with traditional spectral indices, for the classification, provides dimensionality reduction. Moreover, such a method produces classified map which have great correspondence with reality, reflects in high accuracies that were achieved for classes like built-up. The statistics indicated that the classification with object oriented paradigm achieved an overall accuracy of up to 90.1 %, which is far better as compared to automated unsupervised clustering algorithm, such as k-means.

Keywords: Change detection; Urban remote sensing; Object based; GLCM; k-Means; OBIA (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s13198-016-0458-7

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