Incremental Deep Learning for Remote Sensing Image Interpretation

WENG Xingxing; PANG Chao; XU Bowen; XIA Guisong

doi:10.11999/JEIT240172

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WENG Xingxing, PANG Chao, XU Bowen, XIA Guisong. Incremental Deep Learning for Remote Sensing Image Interpretation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240172

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WENG Xingxing, PANG Chao, XU Bowen, XIA Guisong. Incremental Deep Learning for Remote Sensing Image Interpretation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240172

WENG Xingxing, PANG Chao, XU Bowen, XIA Guisong. Incremental Deep Learning for Remote Sensing Image Interpretation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240172

Citation:

WENG Xingxing, PANG Chao, XU Bowen, XIA Guisong. Incremental Deep Learning for Remote Sensing Image Interpretation[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240172

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Incremental Deep Learning for Remote Sensing Image Interpretation

doi: 10.11999/JEIT240172

1.
School of Computer Science, Wuhan University, Wuhan 430072, China
2.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

Funds: The National Natural Science Foundation of China (U22B2011, 62325111)

Received Date: 2024-03-14
Rev Recd Date: 2024-05-14

Available Online: 2024-05-23

Abstract

Abstract

The significant advancement of deep learning has facilitated the emergence of high-precision interpretation models for remote-sensing images. However, a notable drawback is that the majority of interpretation models are trained independently on static datasets, rendering them incapable of adapting to open environments and dynamic demands. This limitation poses a substantial obstacle to the widespread and long-term application of remote-sensing interpretation models. Incremental learning, empowering models to continuously learn new knowledge while retaining previous knowledge, has been recently utilized to drive the evolution of interpretation models and improve their performance. A comprehensive investigation of incremental learning methods for multi-modal remote sensing data and diverse interpretation tasks is provided in this paper. Existing research efforts are organized and reviewed in terms of mitigating catastrophic forgetting and facilitating interpretation model evolution. Drawing from this research progress, this study deliberates on the future research directions for incremental learning in remote sensing, with the aim of advancing research in model evolution for remote sensing image interpretation.
- Remote sensing image interpretation,
- Deep models,
- Incremental learning,
- Model evolution

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References(115)

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