Improving Land Cover Change Detection using Kernel Spectral Angle Mapper Approach in Hyperspectral Images

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Hasanlou Mehdi; Seydi Seyed Teimoor; Seydi Abdoreza

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Journal Paper

Paper Information

Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2018 | Volume:6 | Issue:1 Page(s): 101-116

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Title

Improving Land Cover Change Detection using Kernel Spectral Angle Mapper Approach in Hyperspectral Images

Author(s)

Hasanlou Mehdi | Seydi Seyed Teimoor | Seydi Abdoreza | Issue Writer Certificate

Keywords

Change DetectionQ1

Spectral Angle MapperQ1

Kernel BasedQ1

Hyperspectral ImagesQ1

Expectation-Maximization SegmentationQ1

Land Cover.Logistic RegressionQ1

Abstract

Increasing the population and urban development is one of the most important human actions that cause changes on the face of the earth, especially in the developing countries, which is more. This process can cause devastating effects such as social, economic and biophysical. The harmful effects include; loss of agriculture lands, pasture and forest, change the pattern of the water, which somehow is associated with the changing patterns of land use and land cover. Land use and land cover changes as a basic factor in the changes of the Environment Act and converted into crisis. Identifying and evaluating the potential land-use patterns is essential, that if done on timely and with the high precision, it can help the planners and managers of relevant organizations for more conscious decision and making optimum use of resources in order to prevent the crisis. That would only be possible with the Change Detection. The Hyperspectral Images, due to having high spectral resolution, improved results of changes detection, provide more details of the changes. The main purpose of this research is to improve the process of land-use changes detection using Spectral Angle Mapper algorithm, expectation maximization based on Kernel Based with hyperspectral imagery. The most important advantage of this method are as follow: unsupervised, no need to setting parameters of the knowledge basis, high precision and low false alarms rate. To evaluate the ability of the proposed method, hyperspectral imagery received from agricultural fields of Hermiston in the United States that captured by Hyperion sensors were used. The results are a significant improvement with the use of the proposed method for Change Detection in the standard spectral angle-mapping model compared to the top so that the overall accuracy is 94%, the coefficient Kappa 0. 84 and false alarm rates of less than 6%.

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APA: Copy

Hasanlou, Mehdi, Seydi, Seyed Teimoor, & Seydi, Abdoreza. (2018). Improving Land Cover Change Detection using Kernel Spectral Angle Mapper Approach in Hyperspectral Images. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 6(1 ), 101-116. SID. https://sid.ir/paper/230138/en

Vancouver: Copy

Hasanlou Mehdi, Seydi Seyed Teimoor, Seydi Abdoreza. Improving Land Cover Change Detection using Kernel Spectral Angle Mapper Approach in Hyperspectral Images. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2018;6(1 ):101-116. Available from: https://sid.ir/paper/230138/en

IEEE: Copy

Mehdi Hasanlou, Seyed Teimoor Seydi, and Abdoreza Seydi, “Improving Land Cover Change Detection using Kernel Spectral Angle Mapper Approach in Hyperspectral Images,” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 6, no. 1 , pp. 101–116, 2018, [Online]. Available: https://sid.ir/paper/230138/en

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