Performance analysis of different threshold determination techniques for change vector analysis

2015 ◽  
Vol 86 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Sartajvir Singh ◽  
Rajneesh Talwar
Keyword(s):  
Performance Analysis ◽  
Vector Analysis ◽  
Threshold Determination ◽  
Change Vector Analysis ◽  
Change Vector

scholarly journals Enhancement of the double flexible pace search threshold determination for change vector analysis

2015 ◽  
Vol XL-7/W3 ◽  
pp. 599-603 ◽  
Author(s):  
S. A. Azzouzi ◽  
A. Vidal ◽  
H. A. Bentounes
Keyword(s):  
Visual Information ◽  
Satellite Images ◽  
Training Data ◽  
Vector Analysis ◽  
Detection Methods ◽  
Likelihood Method ◽  
Threshold Determination ◽  
Change Vector Analysis ◽  
Probability Maps ◽  
Change Vector

Remote sensing is one of the most reliable ways to monitor land use and land cover change of large areas. On the other hand, satellite images from different agencies are becoming accessible due to the new user dissemination policies. For that reason, interpretation of remotely sensed data in a spatiotemporal context is becoming a valuable research topic. In the present day, a map of change has a great significant for scientific purposes or planning and management applications. However, it is difficult to extract useful visual information from the large collection of available satellite images. For that reason, automatic or semi-automatic exploration is needed. One of the key stages in the change detection methods is threshold selection. This threshold determination problem has been addressed by several recent techniques based on Change Vector Analysis (CVA). Thus, this work provides a simple semi-automatic procedure that defines the change/no change condition and a comparative study will be involved together with the previous existing method called Double Flexible Pace Search (DFPS). This study uses Landsat Thematic Mapper scenes acquired on different dates in an Algerian region. First, some training data sets containing all possible classes of change are required and their respective supervised posterior probability maps for each scene are obtained. The selected supervised classifier is based on the Maximum Likelihood method. Then four training sets (two sets from each date) are chosen from their corresponding probability maps based on their spatial location in the original images. The optimal average will be obtained as an average of the thresholds obtained at every set. This work verifies that the proposed approach is effective on the selected area, providing improved change map results.

scholarly journals Evaluating the impacts of major cyclonic catastrophes in coastal Bangladesh using geospatial techniques

2021 ◽  
Vol 3 (8) ◽  
Author(s):  
Mashoukur Rahaman ◽  
Md. Esraz-Ul-Zannat
Keyword(s):  
Deep Water ◽  
Water Body ◽  
Crop Production ◽  
Vector Analysis ◽  
Agricultural Crop ◽  
Geospatial Technologies ◽  
Catastrophic Events ◽  
Change Vector Analysis ◽  
Change Vector ◽  
Post Disaster

AbstractCyclonic catastrophes frequently devastate coastal regions of Bangladesh that host around 35 million people which represents two-thirds of the total population. They have caused many problems like agricultural crop loss, forest degradation, damage to built-up areas, river and shoreline changes that are linked to people’s livelihood and ecological biodiversity. There is an absence of a comprehensive assessment of the major cyclonic disasters of Bangladesh that integrates geospatial technologies in a single study. This study aims to integrate geospatial technologies with major disasters and compares them, which has not been tried before. This paper tried to identify impacts that occurred in the coastal region by major catastrophic events at a vast level using different geospatial technologies. It focuses to identify the impacts of major catastrophic events on livelihood and food production as well as compare the impacts and intensity of different disasters. Furthermore, it compared the losses among several districts and for that previous and post-satellite images of disasters that occurred in 1988, 1991, 2007, 2009, 2019 were used. Classification technique like machine learning algorithm was done in pre- to post-disaster images. For quantifying change in the indication of different factors, indices including NDVI, NDWI, NDBI were developed. “Change vector analysis” equation was performed in bands of the images of pre- and post-disaster to identify the magnitude of change. Also, crop production variance was analyzed to detect impacts on crop production. Furthermore, the changes in shallow to deep water were analyzed. There is a notable change in shallow to deep water bodies after each disaster in Satkhira and Bhola district but subtle changes in Khulna and Bagerhat districts. Change vector analysis revealed greater intensity in Bhola in 1988 and Satkhira in 1991. Furthermore, over the years 2007 and 2009 it showed medium and deep intense areas all over the region. A sharp decrease in Aus rice production is witnessed in Barishal in 2007 when cyclone “Sidr” was stricken. The declination of potato production is seen in Khulna district after the 1988 cyclone. A huge change in the land-use classes from classified images like water body, Pasture land in 1988 and water body, forest in 1991 is marked out. Besides, a clear variation in the settlement was observed from the classified images. This study explores the necessity of using more geospatial technologies in disastrous impacts assessment around the world in the context of Bangladesh and, also, emphasizes taking effective, proper and sustainable disaster management and mitigation measures to counter future disastrous impacts.

Improving change vector analysis by cross-correlogram spectral matching for accurate detection of land-cover conversion

2012 ◽  
Vol 34 (4) ◽  
pp. 1127-1145 ◽  
Author(s):  
Chunyang He ◽  
Yuanyuan Zhao ◽  
Jie Tian ◽  
Peijun Shi ◽  
Qingxu Huang
Keyword(s):  
Land Cover ◽  
Vector Analysis ◽  
Spectral Matching ◽  
Change Vector Analysis ◽  
Accurate Detection ◽  
Change Vector
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