Incremental Deep Learning for Remote Sensing Image Interpretation

被引：0
|
作者：
Weng, Xingxing [1 ]
Pang, Chao [1 ]
Xu, Bowen [1 ]
Xia, Guisong [1 ,2 ]
机构：
[1] School of Computer Science, Wuhan University, Wuhan
[2] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Deep models; Incremental learning; Model evolution; Remote sensing image interpretation;
D O I：
10.11999/JEIT240172
中图分类号：
学科分类号：
摘要：
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. © 2024 Science Press. All rights reserved.
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页码：3979 / 4001
页数：22
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