Unsupervised change detection techniques using multispectral satellite images

2003 ◽  
Author(s):  
I. Niemeyer ◽  
M. Canty ◽  
D. Klaus
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Techniques ◽  
Multispectral Satellite Images

scholarly journals Change detection algorithm for multi-temporal satellite images: a review

2018 ◽  
Vol 7 (2.21) ◽  
pp. 206 ◽  
Author(s):  
M Bhavani ◽  
V Hanifar Sangeetha ◽  
K Kalaivani ◽  
K Ulagapriya ◽  
A Saritha
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Principal Component ◽  
Detection Algorithm ◽  
Parameter Method ◽  
Visual Interpretation ◽  
Resource Monitoring ◽  
Gis And Remote Sensing ◽  
Biophysical Parameter ◽  
Detection Techniques

Change detection (CD) is the process of detecting changes from multitemporal satellite images that have undergone spatial changes due to natural and man-made disaster. The objective is to analyse different change detection techniques, in order to use appropriately in various applications with the help of image processing. Techniques that are used in current researches are Image Differencing, Image Regression, Change Vector Analysis (CVA),Principal Component Analysis(PCA), Tasselled Cap, Gramm-Schmidt(GS), Post Classification Comparison, EM Detection, Unsupervised Change Detection, Li-Strahler Reflectance Model, Spectral Mixture Model, Biophysical Parameter Method, Integrated GIS and Remote Sensing Method, GIS Approach, Visual Interpretation and so on. Effective change detection is required for various applications such as rate of deforestation, costal changes, urban developments, damage evaluation, resource monitoring and land disposition.  

scholarly journals Black-Bridge data in the detection of forest area changes in the example of Sudety and Beskidy

2017 ◽  
Vol 59 (4) ◽  
pp. 272-280
Author(s):  
Tomasz Hycza ◽  
Krzysztof Stereńczak ◽  
Radomir Bałazy
Keyword(s):  
Change Detection ◽  
Classification Accuracy ◽  
Satellite Images ◽  
Canopy Cover ◽  
Forest Area ◽  
Classification Analysis ◽  
Detection Techniques ◽  
The Difference ◽  
Short Time ◽  
Ndvi Differencing

Abstract Two change detection techniques (NDVI differencing and post-classification analysis) were compared, in order to detect canopy cover changes in forests on the area of twelve forest districts in the Sudety and West Beskidy Mountains in Poland, using 2012 and 2013 Black-Bridge satellite images. Although the classification accuracy of the respective images was high (about 95%), the accuracy of the difference in bi-temporal images was much worse because of the short time between the dates of images and the imperfection of the algorithm calculating the unclear boundary between the forest and no-forest areas. NDVI differencing method and thresholding brought much better overall results, although roads, clouds and fogs caused much problem performing pseudo-changes.

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