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oapen-20.500.12657-493512021-11-23T14:05:27Z Chapter Deep Learning Training and Benchmarks for Earth Observation Images: Data Sets, Features, and Procedures Schwarz, Gottfried Octavian Dumitru, Corneliu Datcu, Mihai Earth observation, synthetic aperture radar, multispectral, machine learning, deep learning bic Book Industry Communication::U Computing & information technology 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. 2021-06-02T10:13:02Z 2021-06-02T10:13:02Z 2020 chapter ONIX_20210602_10.5772/intechopen.90910_465 https://library.oapen.org/handle/20.500.12657/49351 eng application/pdf n/a 70789.pdf InTechOpen 10.5772/intechopen.90910 10.5772/intechopen.90910 09f6769d-48ed-467d-b150-4cf2680656a1 H2020-MSCA-IF-2017 776193 825258 open access
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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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