Remote sensing data change detection based on the CI test of Bayesian networks

Dai, Qionghai; Ma, Jianwen; Yun, O.

doi:10.1016/j.cageo.2005.06.012

Cited by 21 publications

(5 citation statements)

References 6 publications

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“…In recent years, BNs and, more generally, probabilistic graphical models have been used as a data mining tool in many research fields, but rarely to process remote sensing data. In [25], a BN has been applied to two Landsat TM scenes acquired at different times on the same area, obtaining an overall change detection classification accuracy that overcomes the one obtained by other methods. However, to our knowledge, the capability of BNs to join different kinds of data has been scarcely exploited.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

D’Addabbo

Refice

Pasquariello

et al. 2016

IEEE Trans. Geosci. Remote Sensing

125

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Accurate flood mapping is important for both planning activity during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this work, a Bayesian Network (BN) is proposed to integrate remotely sensed data, such as multi-temporal SAR intensity images and InSAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%

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Section: Introductionmentioning

confidence: 99%

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

D’Addabbo

Refice

Pasquariello

et al. 2016

IEEE Trans. Geosci. Remote Sensing

125

View full text Add to dashboard Cite

show abstract

“…Recent contributions from international literature integrated Landsat data within the study of urban change and urban sprawl in large urban areas (Qin, Jianwen and Yang Yun 2006;Tang, Wang and Yao 2008;Wu and Zhang 2012). Feranec et al (2010) mapped in a synoptic formula the changes in the European landscape from 1990 to 2000 and showed for Romania that Bucharest was one of the most dynamic regions.…”

Section: Introductionmentioning

confidence: 99%

Post-socialist urban growth of Bucharest, Romania – a change detection analysis on Landsat imagery (1984–2010)

Mihai

Nistor

Simion

2015

AGS

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In this study, Bucharest, the capital city of Romania was selected as a case study. Based on time series of Landsat TM imagery and statistical data, an analysis on urban growth from 1984 to 2010 was performed, using an integrated approach of remote sensing and GIS techniques. The land cover data were validated by CORINE Land Cover maps. The results revealed that rapid urban growth of the Bucharest region led to accelerated land use conversion from cropland to built-up land. The processes of deindustrialization in the core city and industrialization to the ring road represent other driving factors for spatiotemporal pattern of built-up land. The paper will discuss these processes and their impact on economic growth and residential suburbanization of the studied region.

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“…In the late 1980s, researchers in artificial intelligence discovered that BNs provided an efficient means for handling uncertainty (Charniak, 1991;Heckerman et al, 1995;Pearl, 1988). Early applications of BNs were in medical diagnosis and genetics, but now their use can also be found in environmental studies (Little et al, 2004;Marcot et al, 2001;Varis, 1998), watershed management (Borsuk et al, 2001), remote sensing (Qin et al, 2006), land-use change (Kocabas and Dragicevic, 2006), land-use decision making (Ma et al, 2004), and urban land market sales (Lei et al, 2005).…”

Section: Bnsmentioning

confidence: 99%

Agent-based model validation using Bayesian networks and vector spatial data

Kocabas

Dragićević

2009

Environ. Plann. B

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Validation of agent-based models (ABMs) of land-use change is a significant challenge in current spatial-modelling research and application. During the validation process, model performance and accuracy assessment depend mostly on pixel-by-pixel comparisons. However, in urban land-use planning problems the use of vector spatial data to develop ABMs is becoming more necessary. Hence, improved and robust validation approaches are required for vector-based ABMs. This study presents a novel validation approach for an ABM using vector-based geographic information system and Bayesian networks. The approach creates a unique-polygons map and an object-oriented database. Three indicator variables are calculated to assess the probability of agreement. The indicator variables are nodes in a Bayesian network that is used to evaluate the final agreement of each unique polygon. Further, an index of overall agreement is calculated. The approach was applied to a simulation outcome map generated by an existing Bayesian network-based agent-system (BNAS) model. The BNAS model simulation of land-use change for the year 2001 was compared with the actual land-use change for the same year using the proposed validation approach. The results obtained indicate that significant agreement between the maps was achieved. The approach is well suited for validating vector-based ABMs and can be used as an aid in model designs for improved model performance.

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Remote sensing data change detection based on the CI test of Bayesian networks

Cited by 21 publications

References 6 publications

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data

Post-socialist urban growth of Bucharest, Romania – a change detection analysis on Landsat imagery (1984–2010)

Agent-based model validation using Bayesian networks and vector spatial data

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