A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery
Abstract The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stack...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-99436-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849314591370117120 |
|---|---|
| author | Qiongbing Xiong Xuecheng Wu Cizhen Yu Hasan Hosseinzadeh |
| author_facet | Qiongbing Xiong Xuecheng Wu Cizhen Yu Hasan Hosseinzadeh |
| author_sort | Qiongbing Xiong |
| collection | DOAJ |
| description | Abstract The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory. Through testing on the Aerial Image Dataset the and UC Merced Land Use Dataset and comparing it with other published works, the approach showed higher classification accuracy compared to current state-of-the-art methods. The study revealed that incorporating boosted Big-Bang Crunch significantly enhances the performance of stacked autoencoder in land-use classification tasks. Moreover, comparisons with other techniques, including convolutional neural networks, Cascaded Residual Dilated Networks, hierarchical convolutional recurrent neural networks, Fusion Region Proposal Networks, and multi-level context-guided classification techniques using Object-Based Convolutional Neural Networks, emphasized the benefits of using Convolutional Neural Network models over traditional methods. The proposed model achieved an accuracy of 92.49% on the AID dataset and 95.93% on the UC Merced dataset, with precision scores of 98.64% and 98.93%, respectively. These results emphasize the importance of integrating deep learning architectures with sophisticated optimization techniques, contributing to enhanced land-use classification accuracy. |
| format | Article |
| id | doaj-art-e74a76f1129b4b589601b9244ecb2568 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e74a76f1129b4b589601b9244ecb25682025-08-20T03:52:24ZengNature PortfolioScientific Reports2045-23222025-05-0115111810.1038/s41598-025-99436-4A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imageryQiongbing Xiong0Xuecheng Wu1Cizhen Yu2Hasan Hosseinzadeh3College of Tourism Management, Guizhou University of CommerceCollege of Tourism Management, Guizhou University of CommerceCollege of Tourism Management, Guizhou University of CommerceArdabil Branch, Islamic Azad UniversityAbstract The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique. The methodology involved utilizing the VGG-19 model for feature extraction, dimensionality reduction, and a stacked autoencoder optimized with a boosted version of the Big Bang Crunch Theory. Through testing on the Aerial Image Dataset the and UC Merced Land Use Dataset and comparing it with other published works, the approach showed higher classification accuracy compared to current state-of-the-art methods. The study revealed that incorporating boosted Big-Bang Crunch significantly enhances the performance of stacked autoencoder in land-use classification tasks. Moreover, comparisons with other techniques, including convolutional neural networks, Cascaded Residual Dilated Networks, hierarchical convolutional recurrent neural networks, Fusion Region Proposal Networks, and multi-level context-guided classification techniques using Object-Based Convolutional Neural Networks, emphasized the benefits of using Convolutional Neural Network models over traditional methods. The proposed model achieved an accuracy of 92.49% on the AID dataset and 95.93% on the UC Merced dataset, with precision scores of 98.64% and 98.93%, respectively. These results emphasize the importance of integrating deep learning architectures with sophisticated optimization techniques, contributing to enhanced land-use classification accuracy.https://doi.org/10.1038/s41598-025-99436-4Land-use classificationDeep learningVGG-19Stacked autoencoderMetaheuristicBoosted Big-Bang crunch theory |
| spellingShingle | Qiongbing Xiong Xuecheng Wu Cizhen Yu Hasan Hosseinzadeh A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery Scientific Reports Land-use classification Deep learning VGG-19 Stacked autoencoder Metaheuristic Boosted Big-Bang crunch theory |
| title | A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery |
| title_full | A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery |
| title_fullStr | A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery |
| title_full_unstemmed | A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery |
| title_short | A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery |
| title_sort | study of combination of autoencoders and boosted big bang crunch theory architectures for land use classification using remotely sensed imagery |
| topic | Land-use classification Deep learning VGG-19 Stacked autoencoder Metaheuristic Boosted Big-Bang crunch theory |
| url | https://doi.org/10.1038/s41598-025-99436-4 |
| work_keys_str_mv | AT qiongbingxiong astudyofcombinationofautoencodersandboostedbigbangcrunchtheoryarchitecturesforlanduseclassificationusingremotelysensedimagery AT xuechengwu astudyofcombinationofautoencodersandboostedbigbangcrunchtheoryarchitecturesforlanduseclassificationusingremotelysensedimagery AT cizhenyu astudyofcombinationofautoencodersandboostedbigbangcrunchtheoryarchitecturesforlanduseclassificationusingremotelysensedimagery AT hasanhosseinzadeh astudyofcombinationofautoencodersandboostedbigbangcrunchtheoryarchitecturesforlanduseclassificationusingremotelysensedimagery AT qiongbingxiong studyofcombinationofautoencodersandboostedbigbangcrunchtheoryarchitecturesforlanduseclassificationusingremotelysensedimagery AT xuechengwu studyofcombinationofautoencodersandboostedbigbangcrunchtheoryarchitecturesforlanduseclassificationusingremotelysensedimagery AT cizhenyu studyofcombinationofautoencodersandboostedbigbangcrunchtheoryarchitecturesforlanduseclassificationusingremotelysensedimagery AT hasanhosseinzadeh studyofcombinationofautoencodersandboostedbigbangcrunchtheoryarchitecturesforlanduseclassificationusingremotelysensedimagery |