Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

Molina, Íñigo; Martínez, Elvira; Morillo, Carmen; Velasco, Jesús; Jara, Alvaro

doi:10.3390/s16101621

Cited by 7 publications

(5 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, image radiometry comparison at pixel level often leads to noisy results. Therefore, other raw image comparison methods use texture information instead, e.g., by computing mutual information between images at two epochs over wider windows (Gueguen, Soille, and Pesaresi 2011;Molina et al 2016). DSM comparison is a specific case among these raw data comparison approaches: it is easier to use in operational situations due to the physical meaning of height differences, and then to define realistic and stable thresholds or to derive change probabilities (Chaabouni-Chouayakh et al 2010;Guerin, Binet, and Pierrot-Deseilligny 2014;Champion et al 2010).…”

Section: Remote Sensing Change Detectionmentioning

confidence: 99%

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database

Liu

Olteanu‐Raimond

Jolivet

et al. 2020

International Journal of Digital Earth

View full text Add to dashboard Cite

Updating an authoritative Land Use and Land Cover (LULC) database requires many resources. Volunteered geographic information (VGI) involves citizens in the collection of data about their spatial environment. There is a growing interest in using existing VGI to update authoritative databases. This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique, in order to update an authoritative land use database. Each VGI data source is considered to be an independent source of information, which is fused together using Dempster-Shafer Theory (DST). The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency. Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles. The data fusion approach achieved an overall accuracy of 85.6% for the 144 features having at least two contributions when the confidence threshold was set to 0.05. Despite the heterogeneity and limited amount of VGI used, the results are promising, with 99% of the LU polygons updated or enriched. These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally.

show abstract

Section: Remote Sensing Change Detectionmentioning

confidence: 99%

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database

Liu

Olteanu‐Raimond

Jolivet

et al. 2020

International Journal of Digital Earth

View full text Add to dashboard Cite

show abstract

“…Changes in the radiometric response or texture observed may indicate LULC changes [21,52]. Because changes in image response can arise from sources other than LULC change, a variety of ways to enhance the analysis have been developed [53][54][55][56]. Specific approaches dedicated to digital surface model comparison also exist.…”

Section: State Of the Artmentioning

confidence: 99%

Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change

et al. 2020

View full text Add to dashboard Cite

Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult.

show abstract

“…As a result of the high performance of the multi-sensor data fusion method on the noise elimination to the process control application [25], it was chosen in order to analyze the control process of a bulk tobacco curing schedule in this study. The multi-sensor data fusion method has been widely used in various research areas [17,26,27,28,29,30,31]. In the field of artificial sensors’ applications, which are highly related to the present study, a feature level fusion with principal component analysis (PCA) feature selection method and several pattern analysis techniques, such as ANN, linear discriminant analysis (LDA), partial least square (PLS), and support vector machine (SVM), have been mostly used for food authentication and the on-line monitoring of food fermentation processes [30,32,33,34].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

Yang

2019

Sensors

View full text Add to dashboard Cite

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.

show abstract

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

Cited by 7 publications

References 60 publications

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database

A data fusion-based framework to integrate multi-source VGI in an authoritative land use database

Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change

Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

Contact Info

Product

Resources

About