MyJournals Home  

RSS FeedsRemote Sensing, Vol. 10, Pages 1251: Land Cover Change Detection Using Multiple Shape Parameters of Spectral and NDVI Curves (Remote Sensing)

 
 

15 august 2018 10:01:21

 
Remote Sensing, Vol. 10, Pages 1251: Land Cover Change Detection Using Multiple Shape Parameters of Spectral and NDVI Curves (Remote Sensing)
 


Spectral and NDVI values have been used to calculate the change magnitudes of land cover, but may result in many pseudo-changes because of inter-class variance. Recently, the shape information of spectral or NDVI curves such as direction, angle, gradient, or other mathematical indicators have been used to improve the accuracy of land cover change detection. However, these measurements, in terms of the single shape features, can hardly capture the complete trends of curves affected by the unsynchronized phenology. Therefore, the calculated change magnitudes are indistinct such that changes and no-changes have a low contrast. This problem has prevented traditional change detection methods from achieving a higher accuracy using bi-temporal images or NDVI time series. In this paper, a multiple shape parameters-based change detection method is proposed by combining the spectral correlation operator and the shape features of NDVI temporal curves (phase angle cumulant, baseline cumulant, relative cumulation rate, and zero-crossing rate). The change magnitude is derived by integrating all the inter-annual differences of these shape parameters. The change regions are discriminated by an automated threshold selection method known as histogram concavity analysis. The results showed that the mean differences in the change magnitudes of the proposed method between 2100 changed and 2523 unchanged pixels was 32%, the overall accuracy was approximately 88%, and the kappa coefficient was 0.76. A comparative analysis was conducted with bi-temporal image-based methods and NDVI time series-based methods, and we demonstrate that the proposed method is more effective and robust than traditional methods in achieving high-contrast change magnitudes and accuracy.


 
86 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 10, Pages 1252: Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping (Remote Sensing)
Remote Sensing, Vol. 10, Pages 1250: Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series (Remote Sensing)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten