A new co-learning method in spatial complex fuzzy inference systems for change detection from satellite images

Giang, Lê Trường; Sơn, Lê Hoàng; Giang, Nguyễn Long; Tuấn, Trần Mạnh; Luong, Nguyen Van; Mai, Dinh Sinh; Selvachandran, Ganeshsree; Gerogiannis, Vassilis C.

doi:10.1007/s00521-022-07928-5

Cited by 5 publications

(8 citation statements)

References 48 publications

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“…The Co-Spatial complex fuzzy inference system (Co-Spatial CFIS+) [29] is a developing version of Mamdani-CFIS based on CFS theory. for change detection from satellite images contains some stages as follows:…”

Section: Co-spatial Complex Fuzzy Inference Systemmentioning

confidence: 99%

“…As discussed in the introduction, Giang et al [29] utilized the FWADAM+ optimization algorithm to optimize the rule parameters in the Co-Spatial CFIS+. However, when forecasting time series problems, the effectiveness of the forecasting system diminishes as the outdated rule system becomes unsuitable for new data.…”

Section: Proposed Adaptive Spatial Complex Fuzzy Inference Systemsmentioning

confidence: 99%

“…The rule space [39] is the space that is calculated by the formula (3) as follows: Aim to reduce the time-consuming of the model; we will group the data with neighbor pixels due to the histogram [40], then determine the rule parameters via the Ternary search [41] and Co-Spatial CFIS+ [29]. The output of this process will produce a new rule base corresponding to the input image.…”

Section: Definitionmentioning

confidence: 99%

“…In recent research, Giang et al [29] proposed a system for handling spatial-temporal data using a CFIS. This system utilized the FWADAM+ optimization algorithm to optimize the rule parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of our work is to adjust the rulebase to adapt to the new images to improve the accuracy and computational time of the previous work, Co-Spatial CFIS. The paper proposes an adaptive spatial complex fuzzy inference system called Spatial CFIS++ to overcome the limitations of existing methods [29] for change detection. The key contributions of this work are summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

et al. 2023

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Fuzzy inference systems, in general, and complex fuzzy inference systems, in particular, play an increasingly important role in many fields, such as change detection, image classification, recognition problems, etc. Despite being the well-known technique to solve with time series data, the rulebase still has the considered limitation because of the directly affecting the results as well as the processing time of these methods. To overcome this limitation, this study proposes an Adaptive spatial complex inference system that can automatically infer and adapt to the new remotely sensed image. In the proposed model, to predict the image of time t + 1, the system will generate a new rulebase according to this expected image. This new rulebase and the previous Co-Spatial-CFIS+ rulebase are evaluated using a complex fuzzy measure. This measure is built by determining the intersection domain between two rule spaces; this intersection value estimates removing, merging, or adding a newly generated rule into the current rulebase. Finally, a more suitable set of rules is obtained for image prediction. To illustrate the efficiency of the proposed approach, it is applied to the remote sensing cloud image data of the U.S. Navy. Our model evaluated the model's effectiveness in comparison to the state-of-the-art along studies in detecting changes in remote sensing cloud images. Moreover, the findings of the experiments revealed that the proposed model could improve the change detection results in terms of R 2 , RMSE, time-consuming, and the number of rules.INDEX TERMS Complex fuzzy inference system; remote sensing images; rule pruning; rule-based system; image change detection.

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Section: Co-spatial Complex Fuzzy Inference Systemmentioning

confidence: 99%

Section: Proposed Adaptive Spatial Complex Fuzzy Inference Systemsmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

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Spatio-temporal analysis of land use/land cover change detection in small regions using self-supervised lightweight deep learning

Naik,

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Meenakshi Sundaram

et al. 2023

Stoch Environ Res Risk Assess

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A Novel Method for Weather Nowcasting Based on Spatial Complex Fuzzy Inference With Multiple Band Input Data

Tuan

Giang

Thong³

et al. 2023

JCC

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The prediction of weather changes, such as rainfall, clouds, floods, and storms, is critical in weather forecasting. There are several sources of input data for this purpose, including radar and observational data, but satellite remote sensing images are the most commonly used due to their ease of collection. In this paper, we present a novel method for weather nowcasting based on Mamdani complex fuzzy inference with multiple band input data. The proposed approach splits the process into two parts: the first part converts the multiple band satellite images into real and imaginary parts to facilitate the rule process, and the second part uses the Spatial CFIS+ algorithm to generate the predicted weather state, taking into account factors such as cloud, wind, and temperature. The use of MapReduce helps to speed up the algorithm's performance. Our experimental results show that this new method outperforms other relevant methods and demonstrates improved prediction accuracy.

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A new co-learning method in spatial complex fuzzy inference systems for change detection from satellite images

Cited by 5 publications

References 48 publications

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures

Spatio-temporal analysis of land use/land cover change detection in small regions using self-supervised lightweight deep learning

A Novel Method for Weather Nowcasting Based on Spatial Complex Fuzzy Inference With Multiple Band Input Data

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