Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures
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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10103877/ |
| _version_ | 1797839167319703552 |
|---|---|
| author | Le Truong Giang Le Hoang Son Nguyen Long Giang Nguyen Van Luong Luong Thi Hong Lan Tran Manh Tuan Nguyen Truong Thang |
| author_facet | Le Truong Giang Le Hoang Son Nguyen Long Giang Nguyen Van Luong Luong Thi Hong Lan Tran Manh Tuan Nguyen Truong Thang |
| author_sort | Le Truong Giang |
| collection | DOAJ |
| description | 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 <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>, RMSE, time-consuming, and the number of rules. |
| first_indexed | 2024-04-09T15:53:45Z |
| format | Article |
| id | doaj.art-29c477a281364e708b5d4249d2232f79 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T15:53:45Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-29c477a281364e708b5d4249d2232f792023-04-25T23:00:32ZengIEEEIEEE Access2169-35362023-01-0111393333935010.1109/ACCESS.2023.326805910103877Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy MeasuresLe Truong Giang0Le Hoang Son1https://orcid.org/0000-0001-6356-0046Nguyen Long Giang2https://orcid.org/0000-0001-6184-1469Nguyen Van Luong3Luong Thi Hong Lan4https://orcid.org/0000-0002-4083-2253Tran Manh Tuan5https://orcid.org/0000-0002-1117-7253Nguyen Truong Thang6https://orcid.org/0000-0002-7110-5622Center of Quality Assurance, Hanoi University of Industry, Hanoi, VietnamVNU Information Technology Institute, Vietnam National University (VNU), Hanoi, VietnamInstitute of Information Technology (IoIT), Vietnam Academy of Science and Technology, Hanoi, VietnamCenter of Quality Assurance, Hanoi University of Industry, Hanoi, VietnamFaculty of Computer Science and Engineering, Thuyloi University, Hanoi, VietnamFaculty of Computer Science and Engineering, Thuyloi University, Hanoi, VietnamInstitute of Information Technology (IoIT), Vietnam Academy of Science and Technology, Hanoi, VietnamFuzzy 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 <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>, RMSE, time-consuming, and the number of rules.https://ieeexplore.ieee.org/document/10103877/Complex fuzzy inference systemremote sensing imagesrule pruningrule-based systemimage change detection |
| spellingShingle | Le Truong Giang Le Hoang Son Nguyen Long Giang Nguyen Van Luong Luong Thi Hong Lan Tran Manh Tuan Nguyen Truong Thang Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures IEEE Access Complex fuzzy inference system remote sensing images rule pruning rule-based system image change detection |
| title | Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures |
| title_full | Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures |
| title_fullStr | Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures |
| title_full_unstemmed | Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures |
| title_short | Adaptive Spatial Complex Fuzzy Inference Systems With Complex Fuzzy Measures |
| title_sort | adaptive spatial complex fuzzy inference systems with complex fuzzy measures |
| topic | Complex fuzzy inference system remote sensing images rule pruning rule-based system image change detection |
| url | https://ieeexplore.ieee.org/document/10103877/ |
| work_keys_str_mv | AT letruonggiang adaptivespatialcomplexfuzzyinferencesystemswithcomplexfuzzymeasures AT lehoangson adaptivespatialcomplexfuzzyinferencesystemswithcomplexfuzzymeasures AT nguyenlonggiang adaptivespatialcomplexfuzzyinferencesystemswithcomplexfuzzymeasures AT nguyenvanluong adaptivespatialcomplexfuzzyinferencesystemswithcomplexfuzzymeasures AT luongthihonglan adaptivespatialcomplexfuzzyinferencesystemswithcomplexfuzzymeasures AT tranmanhtuan adaptivespatialcomplexfuzzyinferencesystemswithcomplexfuzzymeasures AT nguyentruongthang adaptivespatialcomplexfuzzyinferencesystemswithcomplexfuzzymeasures |