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| 001 | 911346 | ||
| 005 | 20230123110735.0 | ||
| 024 | 7 | _ | |a 10.1080/01431161.2022.2131481 |2 doi |
| 024 | 7 | _ | |a 0143-1161 |2 ISSN |
| 024 | 7 | _ | |a 1366-5901 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2022-04638 |
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| 100 | 1 | _ | |a Bazi, Yakoub |0 0000-0001-9287-0596 |b 0 |
| 245 | _ | _ | |a Learning from Data for Remote Sensing Image Analysis |
| 260 | _ | _ | |a London |c 2022 |b Taylor & Francis |
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| 520 | _ | _ | |a Recent advances in satellite technology have led to a regular, frequent and high- resolution monitoring of Earth at the global scale, providing an unprecedented amount of Earth observation (EO) data. The growing operational capability of global Earth monitoring from space provides a wealth of information on the state of our planet Earth that waits to be mined for several different EO applications, e.g. climate change analysis, urban area studies, forestry applications, risk and damage assessment, water quality assessment, crop monitoring and so on. Recent studies in machine learning have triggered substantial performance gain for the above-mentioned tasks. Advanced machine learning models such as deep convolutional neural networks (CNNs), recursive neural networks and transformers have recently made great progress in a wide range of remote sensing (RS) tasks, such as object detection, RS image classification, image captioning and so on. The study of Bai et al. (2021) analyzes the research progress, hotspots, trends and methods in the field of deep learning in remote sensing, and deep learning is becoming an important tool for remote sensing and has been widely used in numerous remote sensing tasks related to image processing and analysis. In this context, the present special issue aims at gathering a collection of papers in the most advanced and trendy areas dealing with learning from data and with applications to remote sensing image analysis. The manuscripts can be subdivided into five groups depending mainly on the processing or learning task. A specific collection for hyperspectral imagery has been included given the special attention by the remote sensing community to this kind of data. |
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| 700 | 1 | _ | |a Cavallaro, Gabriele |0 P:(DE-Juel1)171343 |b 1 |
| 700 | 1 | _ | |a Demir, Begüm |0 0000-0003-2175-7072 |b 2 |
| 700 | 1 | _ | |a Melgani, Farid |0 0000-0001-9745-3732 |b 3 |e Corresponding author |
| 773 | _ | _ | |a 10.1080/01431161.2022.2131481 |g Vol. 43, no. 15-16, p. 5527 - 5533 |0 PERI:(DE-600)1497529-4 |n 15-16 |p 5527 - 5533 |t International journal of remote sensing |v 43 |y 2022 |x 0143-1161 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/911346/files/preprint.pdf |y Published on 2022-11-12. Available in OpenAccess from 2023-11-12. |
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