CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification
By Haifeng Li, Hao Jiang, Xin Gu, Jian Peng, Wenbo Li, Liang Hong, Chao Tao. 2020
66 citations recorded by the source corpus.
Remote sensing image scene classification has a high application value in the agricultural, military, as well as other fields. A large amount of remote sensing data is obtained every day. After learning the new batch data, scene classification algorithms based on deep learning face the problem of catastrophic forgetting, that is, they cannot maintain the performance of the old batch data. Therefore, it has become more and more important to ensure that the scene classification model has the ability of continual learning, that is, to learn new batch data without forgetting the performance of the old batch data. However, the existing remote sensing image scene classification datasets all use static benchmarks and lack the standard to divide the datasets into a number of sequential learning training batches, which largely limits the development of continual learning in remote sensing image scene classification. First, this study gives the criteria for training batches that have been partitioned into three continual learning scenarios, and proposes a large-scale remote sensing image scene classification database called the Continual Learning Benchmark for Remote Sensing (CLRS). The goal of CLRS is to help develop state-of-the-art continual learning algorithms in the field of remote sensing image scene classification. In addition, in this paper, a new method of constructing a large-scale remote sensing image classification database based on the target detection pretrained model is proposed, which can effectively reduce manual annotations. Finally, several mainstream continual learning methods are tested and analyzed under three continual learning scenarios, and the results can be used as a baseline for future work.
Subfields: Artificial Intelligence, Global and Planetary Change, Media Technology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Valerio Marsocci
- Simone Scardapane
- Nassim Ammour
- S. Divakar Bhat
- Subhasis Chaudhuri
- Naif Alajlan
- Yakoub Bazi
- Tong Zhang
In Media Technology
- Simone Scardapane
- Valerio Marsocci
- Nassim Ammour
- Subhasis Chaudhuri
- Naif Alajlan
- Yakoub Bazi
- Tong Zhang
- Wei Zhang
Citations in the corpus
- Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation (2022, background): “Continual learning benchmark for remote sensing [64] is a large-scale remote sensing image scene classification database based on three CL scenarios.”
- EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, methodology): “For zero-shot classification, all the images from the CLRS [18] and NaSC-TG2 [19] datasets are employed as the zero-shot testing set.”
- Continual Learning Approach for Remote Sensing Scene Classification (2020, background): “In a more recent work [12], the authors have introduced a remote sensing data set for continual learning and explored some baseline methods.”
- CILEA-NET: Curriculum-Based Incremental Learning Framework for Remote Sensing Image Classification (2021, background): “In [32], the authors have tried to introduce a large-scale remote sensing scene classification benchmark to help develop incremental learning algorithms in the field of remote sensing image scene classification.”