Automatic change detection in remotely sensed hyperspectral imagery (Case study: wetlands and waterbodies)

Document Type : Original Article

Authors

School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran

Abstract

Wetlands are one of the important types of ecosystems that play a fundamental role in the environment and
provide significant benefits due to the resources that they contain. Therefore, it is necessary to monitor the
changes in these ecosystems. The alterations in Earth’s ecosystems caused by the natural activities, such as
drought, as well as human activities and population growth has been affecting the wetlands and waterbodies
area. Therefore, for achieving a better detection of these changes over time, it is important to generate
descriptive location maps based on the characteristics of wetlands. Hyperspectral images have shown
potential use in many applications due to their high spectral resolution, and consequently, their high
informative value. This study presents a hybrid procedure for automatic detection of changes in wetlands
using a new approach which can provide more details about the changes with high accuracy. The hybrid
proposed method is based on incorporating chronochrome, Z-score analysis, Otsu algorithm, simplex via
split augmented lagrangian (SISAL), Harsanyi–Farrand–Chang (HFC), Pearson correlation coefficient
(PCC), and support vector machine (SVM) to detect changes using hyperspectral imagery. The proposed
method in the first step, produce a training data for tuning SVM and kernel parameters. The second step,
predicted change areas based on a chronochrome algorithm and binary change map obtained using SVM
classifier. The third step, the amplitude of changes is created by Z-Score analysis and binary change mask.
Finally, the multiple change map is produced based on the estimation of number and extraction of
endmembers and similarity measure. The proposed method evaluated and compared the performances with
other common hyperspectral change detection methods using three real-world datasets of multi-temporal
hyperspectral imagery. The empirical results reveal the superiority of the proposed hybrid method in
extracting the change map with an overall accuracy of nearly 96% and a kappa coefficient of 0.89 while
other hyperspectral change detection methods have the overall accuracy lower than 93% and kappa
coefficient 0.80. In addition, this hybrid method can provide ‘multiple changes’ as well as the magnitude of
extracted changes.

Keywords

Main Subjects

References

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Yuen, P. W., & Richardson, M. (2010). An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition. The Imaging Science Journal, 58(5), 241–253.
Zanotta, D. C., Zani, H., & Shimabukuro, Y. E. (2013). Automatic detection of burned areas in wetlands by remote sensing multitemporal images. Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International (pp. 1959–1962). IEEE.
Zhao, H., Cui, B., Zhang, H., Fan, X., Zhang, Z., & Lei, X. (2010). A landscape approach for wetland change detection (1979-2009) in the Pearl River Estuary. Procedia Environmental Sciences, 2, 1265–1278.
Earth Observation and Geomatics Engineering
Volume 2, Issue 1
June 2018
Pages 9-25

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