Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models
Artificial intelligence (AI) has become the mainstream of analysis tools in remote sensing. Various semantic segmentation models have been introduced to segment land cover from aerial or satellite images, and remarkable results have been achieved. However, they often lack universal performance on un...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/15/2669 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849405950908170240 |
|---|---|
| author | Jae Young Chang Kwan-Young Oh Kwang-Jae Lee |
| author_facet | Jae Young Chang Kwan-Young Oh Kwang-Jae Lee |
| author_sort | Jae Young Chang |
| collection | DOAJ |
| description | Artificial intelligence (AI) has become the mainstream of analysis tools in remote sensing. Various semantic segmentation models have been introduced to segment land cover from aerial or satellite images, and remarkable results have been achieved. However, they often lack universal performance on unseen images, making them challenging to provide as a service. One of the primary reasons for the lack of robustness is overfitting, resulting from errors and inconsistencies in the ground truth (GT). In this study, we propose a method to mitigate these inconsistencies by utilizing redundant models and verify the improvement using a public dataset based on Google Earth images. Redundant models share the same network architecture and hyperparameters but are trained with different combinations of training and validation data on the same dataset. Because of the variations in sample exposure during training, these models yield slightly different inference results. This variability allows for the estimation of pixel-level confidence levels for the GT. The confidence level is incorporated into the GT to influence the loss calculation during the training of the enhanced model. Furthermore, we implemented a consensus model that employs modified masks, where classes with low confidence are substituted by the dominant classes identified through a majority vote from the redundant models. To further improve robustness, we extended the same approach to fuse the dataset with different class compositions based on imagery from the Korea Multipurpose Satellite 3A (KOMPSAT-3A). Performance evaluations were conducted on three network architectures: a simple network, U-Net, and DeepLabV3. In the single-dataset case, the performance of the enhanced and consensus models improved by an average of 2.49% and 2.59% across the network architectures. In the multi-dataset scenario, the enhanced models and consensus models showed an average performance improvement of 3.37% and 3.02% across the network architectures, respectively, compared to an average increase of 1.55% without the proposed method. |
| format | Article |
| id | doaj-art-609536bcc2ee4a68999f0cf773ac276a |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-609536bcc2ee4a68999f0cf773ac276a2025-08-20T03:36:32ZengMDPI AGRemote Sensing2072-42922025-08-011715266910.3390/rs17152669Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant ModelsJae Young Chang0Kwan-Young Oh1Kwang-Jae Lee2National Satellite Operation & Application Center, Korea Aerospace Research Institute, Daejeon 34133, Republic of KoreaNational Satellite Operation & Application Center, Korea Aerospace Research Institute, Daejeon 34133, Republic of KoreaNational Satellite Operation & Application Center, Korea Aerospace Research Institute, Daejeon 34133, Republic of KoreaArtificial intelligence (AI) has become the mainstream of analysis tools in remote sensing. Various semantic segmentation models have been introduced to segment land cover from aerial or satellite images, and remarkable results have been achieved. However, they often lack universal performance on unseen images, making them challenging to provide as a service. One of the primary reasons for the lack of robustness is overfitting, resulting from errors and inconsistencies in the ground truth (GT). In this study, we propose a method to mitigate these inconsistencies by utilizing redundant models and verify the improvement using a public dataset based on Google Earth images. Redundant models share the same network architecture and hyperparameters but are trained with different combinations of training and validation data on the same dataset. Because of the variations in sample exposure during training, these models yield slightly different inference results. This variability allows for the estimation of pixel-level confidence levels for the GT. The confidence level is incorporated into the GT to influence the loss calculation during the training of the enhanced model. Furthermore, we implemented a consensus model that employs modified masks, where classes with low confidence are substituted by the dominant classes identified through a majority vote from the redundant models. To further improve robustness, we extended the same approach to fuse the dataset with different class compositions based on imagery from the Korea Multipurpose Satellite 3A (KOMPSAT-3A). Performance evaluations were conducted on three network architectures: a simple network, U-Net, and DeepLabV3. In the single-dataset case, the performance of the enhanced and consensus models improved by an average of 2.49% and 2.59% across the network architectures. In the multi-dataset scenario, the enhanced models and consensus models showed an average performance improvement of 3.37% and 3.02% across the network architectures, respectively, compared to an average increase of 1.55% without the proposed method.https://www.mdpi.com/2072-4292/17/15/2669satellite imageryKOMPSATdeep-learningland cover segmentationoverfittingsemantic segmentation |
| spellingShingle | Jae Young Chang Kwan-Young Oh Kwang-Jae Lee Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models Remote Sensing satellite imagery KOMPSAT deep-learning land cover segmentation overfitting semantic segmentation |
| title | Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models |
| title_full | Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models |
| title_fullStr | Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models |
| title_full_unstemmed | Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models |
| title_short | Improving the Universal Performance of Land Cover Semantic Segmentation Through Training Data Refinement and Multi-Dataset Fusion via Redundant Models |
| title_sort | improving the universal performance of land cover semantic segmentation through training data refinement and multi dataset fusion via redundant models |
| topic | satellite imagery KOMPSAT deep-learning land cover segmentation overfitting semantic segmentation |
| url | https://www.mdpi.com/2072-4292/17/15/2669 |
| work_keys_str_mv | AT jaeyoungchang improvingtheuniversalperformanceoflandcoversemanticsegmentationthroughtrainingdatarefinementandmultidatasetfusionviaredundantmodels AT kwanyoungoh improvingtheuniversalperformanceoflandcoversemanticsegmentationthroughtrainingdatarefinementandmultidatasetfusionviaredundantmodels AT kwangjaelee improvingtheuniversalperformanceoflandcoversemanticsegmentationthroughtrainingdatarefinementandmultidatasetfusionviaredundantmodels |