Deep Learning Innovations: ResNet Applied to SAR and Sentinel-2 Imagery
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| Publicado en: | Remote Sensing vol. 17, no. 12 (2025), p. 1961-1999 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17121961 |2 doi | |
| 035 | |a 3223939992 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Bilotta Giuliana | |
| 245 | 1 | |a Deep Learning Innovations: ResNet Applied to SAR and Sentinel-2 Imagery | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The elevated precision of data regarding the Earth’s surface, facilitated by the enhanced interoperability among various GNSSs (Global Navigation Satellite Systems), enables the classification of land use and land cover (LULC) via satellites equipped with optical sensors, such as Sentinel-2 of the Copernicus program, which is crucial for land use management and environmental planning. Likewise, data from SAR satellites, such Copernicus’ Sentinel-1 and Jaxa’s ALOS PALSAR, provide diverse environmental investigations, allowing different types of spatial information to be analysed thanks to the particular features of analysis based on radar. Nonetheless, in optical satellites, the relatively low resolution of Sentinel-2 satellites may impede the precision of supervised AI classifiers, crucial for ongoing land use monitoring, especially during the training phase, which can be expensive due to the requirement for advanced technology and extensive training datasets. This project aims to develop an AI classifier utilising high-resolution training data and the resilient architecture of ResNet, in conjunction with the Remote Sensing Image Classification Benchmark (RSI-CB128). ResNet, noted for its deep residual learning capabilities, significantly enhances the classifier’s proficiency in identifying intricate patterns and features from high-resolution images. A test dataset derived from Sentinel-2 raster images is utilised to evaluate the effectiveness of the neural network (NN). Our goals are to thoroughly assess and confirm the efficacy of an AI classifier utilised on high-resolution Sentinel-2 photos. The findings indicate substantial enhancements compared to current classification methods, such as U-Net, Vision Transformer (ViT), and OBIA, underscoring ResNet’s transformative capacity to elevate the precision of land use classification. | |
| 653 | |a Spatial analysis | ||
| 653 | |a Accuracy | ||
| 653 | |a Image resolution | ||
| 653 | |a Earth surface | ||
| 653 | |a Earth | ||
| 653 | |a Remote sensing | ||
| 653 | |a Image processing | ||
| 653 | |a Land use | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Machine learning | ||
| 653 | |a Land cover | ||
| 653 | |a Satellites | ||
| 653 | |a Deep learning | ||
| 653 | |a Radiation | ||
| 653 | |a Agriculture | ||
| 653 | |a Environmental management | ||
| 653 | |a Datasets | ||
| 653 | |a Spatial data | ||
| 653 | |a Neural networks | ||
| 653 | |a Infrastructure | ||
| 653 | |a Land use management | ||
| 653 | |a Classification | ||
| 653 | |a Sensors | ||
| 653 | |a Empowerment | ||
| 653 | |a High resolution | ||
| 653 | |a Effectiveness | ||
| 653 | |a Image classification | ||
| 653 | |a Land management | ||
| 653 | |a Drones | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Optical measuring instruments | ||
| 653 | |a Land use classification | ||
| 653 | |a Land use planning | ||
| 653 | |a Environmental planning | ||
| 653 | |a Global navigation satellite system | ||
| 700 | 1 | |a Bibbò Luigi | |
| 700 | 1 | |a Meduri, Giuseppe M | |
| 700 | 1 | |a Genovese Emanuela | |
| 700 | 1 | |a Barrile Vincenzo | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 12 (2025), p. 1961-1999 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223939992/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223939992/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223939992/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |