Applied Research in Geographical Sciences

Applied Research in Geographical Sciences

Evaluating the accuracy of hyperspectral and multispectral images in wetland cover classification using data mining models (Case study: Shadegan wetland)

Authors
Hamid Bagheri 1
Rahime Rostami 2
1 Department of Civil Engineering, Technical and Vocational University (TVU), Tehran, Iran
2 PhD student, University of Tehran
10.61186/jgs.24.74.22
Abstract
Wetland cover classification is of special importance in order to identify the type of plant species inside the wetland and also to distinguish it from the wetland margin vegetation and to study their ecosystem changes. Due to the spectral similarity between different plant species of wetlands and plants along the wetlands and agricultural lands, this is faced with problems using multispectral data and hyperspectral data can be very useful in this regard. in this study power of hyperspectral and multispectral sensors in identifying the characteristics of the wetland and the ability of ETM + (2011), Hyperion (2011) and ALI (2011) sensors to study the characteristics of Shadegan wetland during 1390 and different spectral indices with a suitable combination of The satellite imagery bands of these sensors were compared as input to a variety of classification methods including maximum likelihood, minimum distance, neural network and support vector machine. The results showed that the support vector machine and neural network methods with closer classification accuracy of 85% in all three images show closer results to reality. The classification accuracy for all three images was at its highest for the backup vector machine method, with a total accuracy of 95.73 for the Hyperion image, 88.03 for the ALI and 89.34 for the ETM +. Therefore, the characteristics considered for the wetland, in the three images obtained from the SVM algorithm showed that showing the differentiation of wetland vegetation use from irrigated agricultural land use is more ambiguous than other wetland features. Studies have shown that this part is less recognizable in ALI and ETM + images than Hyperion images, or in some areas these parts are not separable from aquaculture land at all, while Hyperion due to having 220 bands and having a higher level of Spectral details have the ability to distinguish between the two classes.
Keywords
Superspectral images
Multispectral images
Shadegan wetland
classification
Awad. M., )2018(, Forest mapping: a comparison between hyperspectral and multispectral images and technologies, Journal of Forestry Research volume 29, pages1395–1405.

Burger, J., & Gowen, A. (2011). Data handling in hyperspectral image analysis. Chemometrics and Intelligent Laboratory Systems, 108(1), 13-22.

Borri, D., Caprioli, M., and Tarantino, E. (2005), Spatial information extraction from VHR satellite data to detect land cover transformations. Polytechnic University of Bari, Italy, Pp. 105.

Camps-Valls, G., Tuia, D., Bruzzone, L. and Benediktsson, J., )2014(. Advances in Hyperspectral Image Classification. IEEE Signal Processing Magazine, 31(1): 45–54.

Chaudhry, F., Wu, C.C., Liu, W., Chang, C.I. and Plaza, A.,)2006(. Pixel purity index-based algorithms for endmember extraction from hyperspectral imagery. Recent advances in hyperspectral signal and image processing, 37(2), pp.359-367.

Dixon, B., and Candade, N., )2008(. Multispectral landuse classification using neural networks and support vector Machines: one or the other, or both International J. of Remote Sensing 29 (4), 1185–1206.
Ferrato, L., Forsythe, K.W. )2012(, Comparing Hyperspectral and Multispectral Imagery for Land Classification of the Lower Don River, Toronto, Journal of Geography and Geology; Vol. 5, No. 1, 92-107

Finlayson, C.M., )2003(. The challenge of integrating wetland inventory, assessment and monitoring. Aquat. Conserv. Mar. Freshwat. Ecosyst. 13, 281–286.

Fraser, L. H., & Keddy, P. A. (2005). The future of large wetlands: a global perspective. The World's Largest Wetlands: Ecology and Conservation Eds LH Fraser, PA Keddy (Cambridge University Press, Cambridge) pp, 446-468.

Gheyas, A. and Smith, L.S., )2010(. Feature subset selection in large dimensionality domains. Pattern Recognition, 43(1): 5-13.

Hagner, O., & Reese, H. (2007). A method for calibrated maximum likelihood classification of forest types. Remote sensing of environment, 110(4), 438-444.


Khanna, S., Santos, M. J., Ustin, S. L., Shapiro, K., Haverkamp, P. J., & Lay, M. (2018). Comparing the potential of multispectral and hyperspectral data for monitoring oil spill impact. Sensors, 18(2), 558.

Kim, K. G., Lee, H., & Lee, D. H. (2011). Wetland restoration to enhance biodiversity in urban areas: a comparative analysis. Landscape and Ecological Engineering, 7(1), 27-32.

Krijnen. F. A, John And Rahmani, Shahryar. )2012(. Conservation of Iranian Wetlands Project (CIWP), IRI Department of Environment. Date of 1st Draft Report, 31 December 2012. p 45.

Keshavarz, A. and H. Ghasemiyan Yazdi. )2005(. a fast algorithm based on support vector machine for classification of hyperspectral images using spatial correlation, Iranian journal of electrical engineering and computer engineering, 3: 44-37 (In Persian).

Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: go beyond artificial intelligence. Mobile Networks and Applications, 23(2), 368-375.

Ministry of Cultural Heritage, Tourism and Handicrafts(2019), Shadegan Lagoon, https://www.visitiran.ir/attraction/shadegan-lagoon.

Mountrakis, G., J. Im and C. Ogole. )2011(. Support vector machines in remote sensing: A review. Isprs journal of photogrammetry and Remote Sensing, 13: 247-259.

Okwuashi, O., & Ndehedehe, C. E. (2020). Deep support vector machine for hyperspectral image classification. Pattern Recognition, 103, 107298.

Tso. B. and P.M. Mather. )2009(. Classification Methods for Remotely Sensed Data. Chapter 2-3. 2nd ed., Taylor and Francis Pub., America.

Rizvi, R. H., Sridhar, K. B., Handa, A. K., Chaturvedi, O. P., & Singh, M. (2017). Spectral analysis of hyperion hyperspectral data for identification of mango (Mangifera indica L.) species on farmlands. Indian Journal of Agroforestry, 19(2), 61-64.

Wang, P., Wang, D., Zhang, X., Li, X., Peng, T., Lu, H., & Tian, X. (2020). Numerical and experimental study on the maneuverability of an active propeller control based wave glider. Applied Ocean Research, 104, 102369.
Xing, C., Wang, M., Dong, C., Duan, C., & Wang, Z. (2020). Joint sparse-collaborative representation to fuse hyperspectral and multispectral images. Signal Processing, 173, 107585.