Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley Data
This article presents a comprehensive dataset combining Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission with optical imagery, including RGB and Normalized Difference Vegetation Index (NDVI), from the Sentinel-2 mission. The dataset consists of 8800 images, organized into four fold...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924011223 |
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| author | Ahmed Alejandro Cardona-Mesa Rubén Darío Vásquez-Salazar Luis Gómez Carlos M. Travieso-González Andrés F. Garavito-González Esteban Vásquez-Cano Jean Pierre Díaz-Paz |
| author_facet | Ahmed Alejandro Cardona-Mesa Rubén Darío Vásquez-Salazar Luis Gómez Carlos M. Travieso-González Andrés F. Garavito-González Esteban Vásquez-Cano Jean Pierre Díaz-Paz |
| author_sort | Ahmed Alejandro Cardona-Mesa |
| collection | DOAJ |
| description | This article presents a comprehensive dataset combining Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission with optical imagery, including RGB and Normalized Difference Vegetation Index (NDVI), from the Sentinel-2 mission. The dataset consists of 8800 images, organized into four folders—SAR_VV, SAR_VH, RGB, and NDVI—each containing 2200 images with dimensions of 512 × 512 pixels. These images were collected from various global locations using random geographic coordinates and strict criteria for cloud cover, snow presence, and water percentage, ensuring high-quality and diverse data. The primary motivation for creating this dataset is to address the limitations of optical sensors, which are often hindered by cloud cover and atmospheric conditions. By integrating SAR data, which is unaffected by these factors, the dataset offers a robust tool for a wide range of applications, including land cover classification, vegetation monitoring, and environmental change detection. The dataset is particularly valuable for training machine learning models that require multimodal inputs, such as translating SAR images to optical imagery or enhancing the quality of noisy data. Additionally, the structure of the dataset and the preprocessing steps applied make it readily usable for various research purposes. The SAR images are processed to Level-1 Ground Range Detected (GRD) format, including radiometric calibration and terrain correction, while the optical images are filtered to ensure minimal cloud interference. |
| format | Article |
| id | doaj-art-7d25ff818eb64bf69e510bbd32d3243b |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-7d25ff818eb64bf69e510bbd32d3243b2024-12-01T05:07:21ZengElsevierData in Brief2352-34092024-12-0157111160Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley DataAhmed Alejandro Cardona-Mesa0Rubén Darío Vásquez-Salazar1Luis Gómez2Carlos M. Travieso-González3Andrés F. Garavito-González4Esteban Vásquez-Cano5Jean Pierre Díaz-Paz6Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín, Colombia; Corresponding author at: Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín, Colombia.Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín, ColombiaElectronic Engineering and Automatic Control Department, IUCES, Universidad de Las Palmas de Gran Canaria, Juan de Quesada 30, Las Palmas de Gran Canaria, SpainSignals and Communications Department, IDeTIC, Universidad de Las Palmas de Gran Canaria, Juan de Quesada 30, Las Palmas de Gran Canaria, SpainFaculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín, ColombiaFaculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín, ColombiaFaculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín, ColombiaThis article presents a comprehensive dataset combining Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission with optical imagery, including RGB and Normalized Difference Vegetation Index (NDVI), from the Sentinel-2 mission. The dataset consists of 8800 images, organized into four folders—SAR_VV, SAR_VH, RGB, and NDVI—each containing 2200 images with dimensions of 512 × 512 pixels. These images were collected from various global locations using random geographic coordinates and strict criteria for cloud cover, snow presence, and water percentage, ensuring high-quality and diverse data. The primary motivation for creating this dataset is to address the limitations of optical sensors, which are often hindered by cloud cover and atmospheric conditions. By integrating SAR data, which is unaffected by these factors, the dataset offers a robust tool for a wide range of applications, including land cover classification, vegetation monitoring, and environmental change detection. The dataset is particularly valuable for training machine learning models that require multimodal inputs, such as translating SAR images to optical imagery or enhancing the quality of noisy data. Additionally, the structure of the dataset and the preprocessing steps applied make it readily usable for various research purposes. The SAR images are processed to Level-1 Ground Range Detected (GRD) format, including radiometric calibration and terrain correction, while the optical images are filtered to ensure minimal cloud interference.http://www.sciencedirect.com/science/article/pii/S2352340924011223SentinelSynthetic aperture radar (SAR)SpeckleDeep learningSupervised learningVegetation index |
| spellingShingle | Ahmed Alejandro Cardona-Mesa Rubén Darío Vásquez-Salazar Luis Gómez Carlos M. Travieso-González Andrés F. Garavito-González Esteban Vásquez-Cano Jean Pierre Díaz-Paz Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley Data Data in Brief Sentinel Synthetic aperture radar (SAR) Speckle Deep learning Supervised learning Vegetation index |
| title | Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley Data |
| title_full | Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley Data |
| title_fullStr | Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley Data |
| title_full_unstemmed | Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley Data |
| title_short | Dataset of Sentinel-1 SAR and Sentinel-2 RGB-NDVI imageryMendeley Data |
| title_sort | dataset of sentinel 1 sar and sentinel 2 rgb ndvi imagerymendeley data |
| topic | Sentinel Synthetic aperture radar (SAR) Speckle Deep learning Supervised learning Vegetation index |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924011223 |
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