Fuzzy-fusion approach for land cover classification

Santos, Tiago M. A.; Mora, André; Ribeiro, Rita A.; Silva, João M. N.

doi:10.1109/ines.2016.7555116

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Data fusion approaches, based on fuzzy logic techniques, are emerging as a technique for land classification to perform correct reasoning inferences (Hyder, Shahbazian, and Waltz 2012;Santos, Andre Mora, and Joao 2016). The FIF algorithm -basis of our approachis based on fuzzy logic and specialized decision-making aggregation operators and was applied to spacecraft landing with hazard avoidance (Bourdarias et al 2010;Câmara et al 2015) as well as for land cover classification (A. D. Mora et al 2015;A.…”

Section: Data Fusion Approachesmentioning

confidence: 99%

Data fusion approach for eucalyptus trees identification

Oliveira

Martins

Mora

et al. 2021

International Journal of Remote Sensing

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Remote sensing is based on the extraction of data, acquired by satellites or aircrafts, through multispectral images, that allow their remote analysis and classification. Analysing those images with data fusion techniques is a promising approach for identification and classification of forest types. Fusion techniques can aggregate various sources of heterogeneous information to generate valueadded maps, facilitating forest-type classification. This work applies a data fusion algorithm, denoted FIF (Fuzzy Information Fusion), which combines computational intelligence techniques with multicriteria concepts and techniques, to automatically distinguish Eucalyptus trees from satellite images. The algorithm customization was performed with a Portuguese area planted with Eucalyptus. After customizing and validating the approach with several representative scenarios to assess its suitability for automatic classification of Eucalyptus, we tested on a large tile obtaining a sensitivity of 69.61%, with a specificity of 99.43%, and an overall accuracy of 98.19%. This work demonstrates the potential of our approach to automatically classify specific forest types from satellite images, since this is a novel approach dedicated to the identification of eucalyptus trees.

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Section: Data Fusion Approachesmentioning

confidence: 99%

Data fusion approach for eucalyptus trees identification

Oliveira

Martins

Mora

et al. 2021

International Journal of Remote Sensing

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“…As mentioned before, here, we follow an inference scheme that uses reinforcement aggregation operators to perform the fusion of images [1]. This fusion process with specialized aggregation operators is based on other work performed by some co-authors [18].…”

Section: Reinforcement Inference Schemementioning

confidence: 99%

“…• ⊕ j = aggregation operator; It should be noted that performing inference with reinforcement operators is an innovative method to determine a more positive or negative reinforcement of the rule's firing level and respective classification certainty for each class (for more details, see [1]). In the same article, it was found that the Uninorm reinforcement aggregation operator was better for classification of satellite images; hence, it is the one considered in this comparative study.…”

Section: Reinforcement Inference Schemementioning

confidence: 99%

“…This paper is based on a preliminary conference paper [1] where we presented a novel fuzzy image fusion technique and compared it with two other computational intelligence methods, decision trees and neural networks, for fusing images and performing classification of terrains as waterbody, river bank, bare area, cropland, grassland, shrubland and forest. In this article, we extend the previous work by reproducing a spatiotemporal case study with three years (1989,2002,2005) run by Temudo et al [2] where the changes of land cover usage in the post-war period (>1992) in the district of Mandimba, Niassa province, Mozambique, were studied (http://earthexplorer.usgs.gov).…”

Section: Introductionmentioning

confidence: 99%

“…The discussed fuzzy-fusion inference method, instead of just using a basic fuzzy inference model, is improved with reinforcement aggregation operators to perform the inference reasoning [1,18]. Reinforcement aggregation operators have been applied in decision making methods and data fusion [19][20][21][22], but here, they are used in the inference scheme to ensure positive or negative reinforcement in the classification.…”

Section: Introductionmentioning

confidence: 99%

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Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

et al. 2017

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This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas.

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Image fusion for MODIS and Landsat images using top hat based moving technique with FIS

Swathika¹,

Sharmila²

2018

Cluster Comput

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Fuzzy-fusion approach for land cover classification

Cited by 6 publications

References 23 publications

Data fusion approach for eucalyptus trees identification

Data fusion approach for eucalyptus trees identification

Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

Image fusion for MODIS and Landsat images using top hat based moving technique with FIS

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