# Deep Curriculum Learning for PolSAR Image Classification

arXiv: https://arxiv.org/abs/2112.13426 · HTML: https://mlsanity.com/paper/2112.13426/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2021.

## Built on by (most cited first, 4 of 4)

- [Better, Not Just More: Data-Centric Machine Learning for Earth Observation](https://mlsanity.com/paper/2312.05327/) (2023)
- [Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads](https://mlsanity.com/paper/2204.08364/) (2022)
- [Development and Comparison of Scoring Functions in Curriculum Learning](https://mlsanity.com/paper/2202.06823/) (2022)
- [Towards Robust Handwritten Text Recognition with On-the-fly User Participation](https://mlsanity.com/paper/2212.08834/) (2022)

## Abstract (arXiv)

Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetric synthetic aperture radar (PolSAR) data. This method utilizes the entropy-alpha target decomposition method to estimate the degree of complexity of each PolSAR image patch before applying it to the convolutional neural network (CNN). Also, an accumulative mini-batch pacing function is used to introduce more difficult patches to CNN.Experiments on the widely used data set of AIRSAR Flevoland reveal that the proposed curriculum learning method can not only increase classification accuracy but also lead to faster training convergence.