# Deep Curriculum Learning for PolSAR Image Classification

Source: [Deep Curriculum Learning for PolSAR Image Classification](<https://www.sota2.com/research/paper/68fa7fa51e209131cea9ab54>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T06:51:42.495Z

Authors: Hamidreza Mousavi, Maryam Imani, Hassan Ghassemian

Published: 2021-12-26T18:02:14.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2112.13426>)

[PDF](<https://arxiv.org/pdf/2112.13426.pdf>)