scholarly journals A modified approach for change detection using change vector analysis in posterior probability space

2015 ◽  
Vol XL-7/W3 ◽  
pp. 593-598
Author(s):  
S. A. Azzouzi ◽  
A. Vidal ◽  
H. A. Bentounes
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Probability Space ◽  
Posterior Probability ◽  
Threshold Value ◽  
Thematic Mapper ◽  
Vector Analysis ◽  
Detection Analysis ◽  
Change Vector Analysis ◽  
Change Vector

The multispectral and multitemporal data coming from satellites allow us to extract valuable spatiotemporal change. Consequently, Earth surface change detection analysis has been used in the past to monitor land cover changes caused by different reasons. Several techniques have been used for that purpose and change vector analysis (CVA) has been frequently employed to carry out automatic spatiotemporal information extraction. This work describes a modified methodology based on Supervised Change Vector Analysis in Posterior probability Space (SCVAPS) with the final aim of obtaining a change detection map in Blida, Algeria. The proposed technique is a Modified version of Supervised Change Vector Analysis Posterior probability Space (MSCVAPS) and it is applied at the same region that the original technique studied in the literature. The classical Maximum Likelihood classifier is the selected method for supervised classification since it provides good properties in the posterior probability map. An improved method for threshold determination based on Double Flexible Pace Search (DFPS) is proposed in this work and it is employed to obtain the most adequate threshold value. Then, the MSCVAPS approach is evaluated by two cases study of the land cover change detection in the region of Blida, Algeria, and in the region of Shunyi District, Beijing, China, using a pair of Landsat Thematic Mapper images and pair of Landsat Enhanced Thematic Mapper images, respectively. The final evaluation is given by the overall accuracy of changed and unchanged pixels and the kappa coefficient. The results show that the modified approach gives excellent results using the same area of study that was selected in the literature.

scholarly journals Fusion of Change Vector Analysis in Posterior Probability Space and Postclassification Comparison for Change Detection from Multispectral Remote Sensing Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1511 ◽  
Author(s):  
Fatemeh Zakeri ◽  
Bo Huang ◽  
Mohammad Reza Saradjian
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Probability Space ◽  
Posterior Probability ◽  
Feature Space ◽  
Change Vector Analysis ◽  
Different Types ◽  
Change Vector ◽  
Types Of Change ◽  
Change Map

Postclassification Comparison (PCC) has been widely used as a change-detection method. The PCC algorithm is straightforward and easily applicable to all satellite images, regardless of whether they are acquired from the same sensor or in the same environmental conditions. However, PCC is prone to cumulative error, which results from classification errors. Alternatively, Change Vector Analysis in Posterior Probability Space (CVAPS), which interprets change based on comparing the posterior probability vectors of a pixel, can alleviate the classification error accumulation present in PCC. CVAPS identifies the type of change based on the direction of a change vector. However, a change vector can be translated to a new position within the feature space; consequently, it is not inconceivable that identical measures of direction may be used by CVAPS to describe multiple types of change. Our proposed method identifies land-cover transitions by using a fusion of CVAPS and PCC. In the proposed algorithm, contrary to CVAPS, a threshold does not need to be specified in order to extract change. Moreover, the proposed method uses a Random Forest as a trainable fusion method in order to obtain a change map directly in a feature space which is obtained from CVAPS and PCC. In other words, there is no need to specify a threshold to obtain a change map through the CVAPS method and then combine it with the change map obtained from the PCC method. This is an advantage over other change-detection methods focused on fusing multiple change-detection approaches. In addition, the proposed method identifies different types of land-cover transitions, based on the fusion of CVAPS and PCC, to improve the results of change-type determination. The proposed method is applied to images acquired by Landsat and Quickbird. The resultant maps confirm the utility of the proposed method as a change-detection/labeling tool. For example, the new method has an overall accuracy and a kappa coefficient relative improvement of 7% and 9%, respectively, on average, over CVAPS and PCC in determining different types of change.

Land cover change detection in Crete Island, Greece, using different combinations of biophysical indices in change vector analysis

2020 ◽  
Author(s):  
Christos Polykretis ◽  
Manolis G. Grillakis ◽  
Dimitrios D. Alexakis
Keyword(s):  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Vector Analysis ◽  
Land Cover Changes ◽  
Change Vector Analysis ◽  
Tasseled Cap ◽  
Crete Island ◽  
Change Vector ◽  
Land Cover Change Detection

<p>Land cover describes the general biophysical state of the surface providing also information about other aspects of the land, such as soils and water. Changes in land cover may have noticeable impact on the ecosystem biodiversity, water resources, climate system and socio-economic sectors. Therefore, the need for detecting these changes is more and more imperative, especially given the emergence of unbalances caused by natural and anthropogenic driving forces like climate change, intensive agriculture and wrong land management decisions. Land cover changes are mainly represented by changes in the biophysical properties of land surface. These properties can be measured by remote sensing-derived indices representing both the vegetation and soil conditions of a given region. In this research effort, by applying a change detection technique like change vector analysis (CVA), the relationship between the dynamic changes in such indices and land cover changes in Crete Island, Greece, was assessed and mapped for the time periods of 1999–2009 and 2009–2019. Vegetation indices such as normalized difference vegetation index (NDVI) and tasseled cap greenness (TCG), and soil indices such as albedo and tasseled cap brightness (TCB), were estimated by Landsat satellite images captured in 1999, 2009 and 2019. Based on two different index combinations (NDVI–albedo and TCG–TCB), CVA produced change results for each of the periods indicating the magnitude and type (direction) of changes, respectively. The most appropriate combination for land cover change detection in the study area was determined by an evaluation process resulting to the estimation of accuracy statistics (kappa index and overall accuracy). Although promising accuracy results were provided for both examined combinations, the change maps produced by the combination of NDVI–albedo were found to be more accurate.</p><p><em>Acknowledgments: This research has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology Hellas (GSRT), under Agreement No 651.</em></p>

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