An improved change detection approach using tri-temporal logic-verified change vector analysis

Du, Peijun; Wang, Xin; Chen, Dongmei; Liu, Sicong; Lin, Cong; Meng, Yaping

doi:10.1016/j.isprsjprs.2020.01.026

Cited by 63 publications

(32 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Self-adaptive weight CVA has also been adopted in some studies to cover the various shapes of ground targets [33], [34]. Overall, the classical CVA has been extended in many ways for LCCD with remote sensing images, such as robust CVA (RCVA) [16] which developed to account for pixel neighborhood effects, multi-scale CVA [35], and an improved CVA named tri-temporal logic-verified CVA (TLCVA) [17] which can identify the errors of CVA through logical reasoning and judgment with an additional temporal image assistance. Recently, deep learning has been integrated with CVA for LCCD with remote sensing images, such as a context-sensitive framework called DCVA was developed for exploiting convolutional neural network features [1].…”

Section: Overview Of Cva-based Methodsmentioning

confidence: 99%

“…Then, we give a comprehensive diagnosis on the performance of six selected methods with different datasets. These methods include the classical CVA [13], the CVA coupled with Markov random field (CVA MRF) [14], CVA integrated with spectral angle mapper (CVA SAM) [15], robust CVA (RCVA) [16], deep C-VA (DCVA) [1], and tri-temporal logic-verified CVA (TLCVA) [17], which are widely used for LCCD with remote sensing images. More details about these methods will presented in the following sections.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Diagnostic Analysis on Change Vector Analysis Methods for LCCD Using Remote Sensing Images

Wang

Xie

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Section: Overview Of Cva-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnostic Analysis on Change Vector Analysis Methods for LCCD Using Remote Sensing Images

Wang

Xie

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

“…A decision on change is made based on whether the change magnitude exceeds a specific threshold [15]. It was used to identify changes in pixels between images of two different dates or times [19,29]. This study took 2 bands for each year to use in the equation for five years (1988,1991,2007,2009,2019).…”

Section: Change Vector Analysismentioning

confidence: 99%

“…Change vector analysis (CVA) was used on the satellite images to identify ecological changes in Fujan Province which is located in South-Eastern China [58]. In addition, recently a unique tri-temporal logic-verified change vector analysis (TLCVA) approach was developed that can identify the errors in CVA [19]. Geospatial technology like remote sensing has been used in detecting land-use land-cover change (LULC) in coastal areas of Bangladesh for the past 20 years [43].…”

Section: Introductionmentioning

confidence: 99%

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

Rahaman

Esraz-Ul-Zannat

2021

SN Appl. Sci.

View full text Add to dashboard Cite

Cyclonic 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.

show abstract

“…State-of-the-art change detection methods from pixel-level, object-level and featurelevel have been continually developed. For example, Du et al (2020) proposed a new change vector analysis (CVA) method for tri-temporal logical verification, which can identify CVA errors with the assistance of additional temporal images through logical reasoning and judgement [14]. Sun et al (2021) proposed a change detection method based on structural consistency, which detects changes by comparing the structure of two images rather than comparing the pixel values of the images [15].…”

Section: Introductionmentioning

confidence: 99%

A Land Cover Change Detection Method Combing Spectral Values and Class Probabilities

et al. 2021

View full text Add to dashboard Cite

Land cover change detection plays an important role in natural disaster monitoring, tracking urban expansion, and many social benefit areas. The spectral-based direct comparison (SDC) methods are commonly used for change detection, but such methods are vulnerable to the influence of external factors. In general, land changes among different land cover types have different characters of change magnitude. The class probability-based direct comparison (CPDC) methods consider land type information and reduce the influence of external factors, but these methods are strongly dependent on the training samples. To address the above problems, we proposed a novel change detection method that integrates spectral values and class probabilities (SVCP). First, a new change magnitude map based on spectral values and class probabilities is constructed by using the maximum interclass variance and Gaussian mixture model (GMM), which greatly enhances the differentiation between changed and unchanged areas. Second, the Kapur threshold selection method is improved by using the variance of the changed and unchanged areas as well as the class probabilities for adaptive thresholding. The SVCP approach was assessed by two case studies from Landsat 8 Operational Land Imager (OLI) images. The "change/no-change" detection and "from-to" change types were evaluated. The experimental results indicated that the SVCP method is more accurate in the change detection, with lower false and missed detection rates than the traditional methods.INDEX TERMS Change detection, direct comparison method, Gaussian mixture model, adaptive threshold selection.

show abstract

An improved change detection approach using tri-temporal logic-verified change vector analysis

Cited by 63 publications

References 47 publications

Diagnostic Analysis on Change Vector Analysis Methods for LCCD Using Remote Sensing Images

Diagnostic Analysis on Change Vector Analysis Methods for LCCD Using Remote Sensing Images

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

A Land Cover Change Detection Method Combing Spectral Values and Class Probabilities

Contact Info

Product

Resources

About