Chapter Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures
Deep learning methods are often used for image classification or local object segmentation. The corresponding test and validation data sets are an integral part of the learning process and also of the algorithm performance evaluation. High and particularly very high-resolution Earth observation (EO)...
Збережено в:
| Автори: | , , |
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| Формат: | Online |
| Мова: | Англійська |
| Опубліковано: |
InTechOpen
2021
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| Предмети: | |
| Онлайн доступ: | ONIX_20210602_10.5772/intechopen.90910_465 |
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| Резюме: | Deep learning methods are often used for image classification or local object segmentation. The corresponding test and validation data sets are an integral part of the learning process and also of the algorithm performance evaluation. High and particularly very high-resolution Earth observation (EO) applications based on satellite images primarily aim at the semantic labeling of land cover structures or objects as well as of temporal evolution classes. However, one of the main EO objectives is physical parameter retrievals such as temperatures, precipitation, and crop yield predictions. Therefore, we need reliably labeled data sets and tools to train the developed algorithms and to assess the performance of our deep learning paradigms. Generally, imaging sensors generate a visually understandable representation of the observed scene. However, this does not hold for many EO images, where the recorded images only depict a spectral subset of the scattered light field, thus generating an indirect signature of the imaged object. This spots the load of EO image understanding, as a new and particular challenge of Machine Learning (ML) and Artificial Intelligence (AI). This chapter reviews and analyses the new approaches of EO imaging leveraging the recent advances in physical process-based ML and AI methods and signal processing. |
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