Decision Fusion of Remote-Sensing Data for Land Cover Classification

Bris, Arnaud Le; Chehata, Nesrine; Ouerghemmi, Walid; Wendl, Cyril; Postadjian, Tristan; Puissant, A; Mallet, Clément

doi:10.1016/b978-0-12-817358-9.00017-2

Cited by 8 publications

(4 citation statements)

References 71 publications

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“…To increase the classification accuracy of land crops, the fusion-based voting strategy at the decision level was presented with a set of land crop maps derived from several scenarios (binary combinations of the feature selection and classification algorithms) and the results showed that by implementing the voting strategy, land crop map accuracy was significantly improved. In some of previous studies, fusion at the decision-level was used to improve the accuracy of urban land use/cover maps [ 38 , 39 , 40 , 41 ]. One of the limitations of the method presented in this study for land crop classification is the high implementation time and processing volume.…”

Section: Discussionmentioning

confidence: 99%

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy

Fathololoumi

Firozjaei

Biswas

2022

Sensors

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The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features’ capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.

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Section: Discussionmentioning

confidence: 99%

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy

Fathololoumi

Firozjaei

Biswas

2022

Sensors

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“…For each per epoch classification, each pixel contains a value corresponding to its class. To synthesize this information to a unique map, one can rely on the Fusion by Voting strategy (Le Bris et al, 2019). It consists in merging classifications by letting each of them vote for its label and choosing the final label as the winner of this majority vote.…”

Section: Classification Fusionmentioning

confidence: 99%

Classification of Vegetation Classes by Using Time Series of Sentinel-2 Images for Large Scale Mapping in Cameroon

Tagne

Bris²,

Monkam³

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Sentinel-2 satellites provide dense image time series exhibiting high spectral, spatial and temporal resolutions. These images are in particular of utter interest for Land-Cover (LC) mapping at large scales. LC maps can now be computed on a yearly basis at the scale of a country with efficient supervised classifiers, assuming suitable training data are available. However, the efficient exploitation of large amount of Sentinel-2 imagery still remain challenging on unexplored areas where state-of-the-art classifiers are prone to fail. This paper focuses on Land-Cover mapping over Cameroon for the purpose of updating the Very High Resolution national topographic geodatabase. The ι2 framework is adopted and tested for the specificity of the country. Here, experiments focus on generic vegetation classes (five) which enables providing robust focusing masks for higher resolution classifications. Two strategies are compared: (i) a LC map is calculated out of a year long time series and (ii) monthly LC maps are generated and merged into a single yearly map. Satisfactory accuracy scores are obtained (>94% in Overall Accuracy), allowing to provide a first step towards finer-grained map retrieval.

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“…On the other hand, for heterogeneous and complex multimodal sensing, DF can be divided into three abstraction levels: (1) data level fusion, (2) feature level fusion, and (3) decision level fusion [80], [81], [82], [83], [84]. Figure 3 summarizes the taxonomy of DF traffic parameters/variables in the TFA studies.…”

Section: Figure 2 General Df Modelmentioning

confidence: 99%

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

et al. 2021

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Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.INDEX TERMS Intelligent transportation systems, traffic flow analysis, data fusion; real-time processing, multi-sensor, heterogeneous data, machine learning.

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Decision Fusion of Remote-Sensing Data for Land Cover Classification

Cited by 8 publications

References 71 publications

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy

An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy

Classification of Vegetation Classes by Using Time Series of Sentinel-2 Images for Large Scale Mapping in Cameroon

Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

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