# Efficient Curriculum based Continual Learning with Informative Subset Selection for Remote Sensing Scene Classification

arXiv: https://arxiv.org/abs/2309.01050 · HTML: https://mlsanity.com/paper/2309.01050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 3 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence](https://mlsanity.com/paper/1801.10112/) (2018)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Online Continual Learning with Maximally Interfered Retrieval](https://mlsanity.com/paper/1908.04742/) (2019)
- [Online Continual Learning in Image Classification: An Empirical Survey](https://mlsanity.com/paper/2101.10423/) (2021)
- [A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning](https://mlsanity.com/paper/2001.00689/) (2020)
- [Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting](https://mlsanity.com/paper/1802.02950/) (2018)
- [PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval](https://mlsanity.com/paper/1706.03424/) (2017)
- [Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data](https://mlsanity.com/paper/1810.12448/) (2018)
- [Mondrian Forests: Efficient Online Random Forests](https://mlsanity.com/paper/1406.2673/) (2014)

## Abstract (arXiv)

We tackle the problem of class incremental learning (CIL) in the realm of landcover classification from optical remote sensing (RS) images in this paper. The paradigm of CIL has recently gained much prominence given the fact that data are generally obtained in a sequential manner for real-world phenomenon. However, CIL has not been extensively considered yet in the domain of RS irrespective of the fact that the satellites tend to discover new classes at different geographical locations temporally. With this motivation, we propose a novel CIL framework inspired by the recent success of replay-memory based approaches and tackling two of their shortcomings. In order to reduce the effect of catastrophic forgetting of the old classes when a new stream arrives, we learn a curriculum of the new classes based on their similarity with the old classes. This is found to limit the degree of forgetting substantially. Next while constructing the replay memory, instead of randomly selecting samples from the old streams, we propose a sample selection strategy which ensures the selection of highly confident samples so as to reduce the effects of noise. We observe a sharp improvement in the CIL performance with the proposed components. Experimental results on the benchmark NWPU-RESISC45, PatternNet, and EuroSAT datasets confirm that our method offers improved stability-plasticity trade-off than the literature.