Change detection for high-resolution remote sensing imagery using object-oriented change vector analysis method

Abstract. The extraction and timely updating of land use /cover information is a key issue in remote sensing change detection. The change vector analysis (CVA) is a better method of change detection. However, the CVA method is the blindness of artificial choice of threshold. Moreover, the direction cosine of CVA cannot represent the unique point in change vector space and it can’t distinguish the change category effectively. In order to avoid this defect, the midline vector is added to CVA method. In this paper, we use the midline change vector analysis (MCVA) method to detect the land use /cover change in multi temporal remote sensing images. We proposed the two-step threshold method to get the optimal threshold and determine the change and the unchanged region of the difference remote sensing image. We chose Hefei city of Anhui Province as the study area, and adopted two Landsat5 TM images in 2000 and 2008 year as experiment data. We use the MCVA and two-step threshold method to achieve remote sensing change detection. In order to compare the detection accuracy between MCVA method and the traditional post classification comparison method, the paper choose the same area (178 pixels × 180 pixels) in the two images to analyse the accuracy, and compare the accuracy of MCVA method with that of the traditional post classification comparison method based on SVM. The experiment results show that the MCVA method has higher overall accuracy, lower allocation disagreement and quantity disagreement. What’s more, the overall accuracy of MCVA method can reach nearly 60%, much higher than the traditional post classification comparison method (less than 40%). And the MCVA method can effectively avoid the problem of change vector direction cosine values are not unique, and the result is much better than the traditional post classification (SVM) comparison method. It indicates that MCVA is a more effective method in land use / cover change detection for middle resolution multispectral images.

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Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis

Remote Sensing ◽

10.3390/rs11202345 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2345 ◽

Cited By ~ 19

Author(s):

Hanqiu Xu ◽

Yifan Wang ◽

Huade Guan ◽

Tingting Shi ◽

Xisheng Hu

Keyword(s):

Remote Sensing ◽

Time Series ◽

Change Detection ◽

Ecological Status ◽

Vector Analysis ◽

Ecological Index ◽

Ecological Conditions ◽

Change Vector Analysis ◽

Ecological Changes ◽

Change Vector

Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales.

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Change vector analysis method for inundation change detection using multi-temporal multi-polarized SAR images

10.1117/12.739265 ◽

2007 ◽

Cited By ~ 1

Author(s):

Guozhuang Shen ◽

Huadong Guo ◽

Jingjuan Liao

Keyword(s):

Change Detection ◽

Vector Analysis ◽

Analysis Method ◽

Sar Images ◽

Change Vector Analysis ◽

Multi Temporal ◽

Change Vector

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Change Detection from Landsat TM Images Using Change Vector Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.268-270.590 ◽

2011 ◽

Vol 268-270 ◽

pp. 590-594

Author(s):

Xiao Lu Song ◽

Bo Cheng ◽

Teng Fei Long

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Focal Point ◽

Spectral Characteristics ◽

Direction Cosine ◽

Vector Analysis ◽

Detection Methods ◽

Variable Step Size ◽

Change Vector Analysis ◽

Change Vector

Change detection is a process of extracting, analyzing, and defining change information from remote sensing imageries. At present, remote sensing change detection methods are mainly classified into two categories, one based on the spectral characteristics of the type of approach, and the other is spectral change vector analysis. In this paper, a simplified threshold variable step-size search which can be used on determining changes in the threshold vector is adopted, as well as the supervised classification technique in the direction cosine space with the type of focal point in the initial assay vector remote sensing images. Results are discussed in the last part of this paper, which show that CVA can extract change information effectively in our study area of Wuhan city.

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