王定盼, 董小环, 黄令勇, 王晓华, 李庆君, 季顺平. 基于信息熵不确定性估计的域自适应多源遥感影像地表覆盖分类[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220346
引用本文: 王定盼, 董小环, 黄令勇, 王晓华, 李庆君, 季顺平. 基于信息熵不确定性估计的域自适应多源遥感影像地表覆盖分类[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20220346
WANG Dingpan, DONG Xiaohuan, HUANG Lingyong, WANG Xiaohua, LI Qingjun, JI Shunping. Information entropy uncertainty estimation based domain adaptation for land cover classification from multi-source remote sensing images[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220346
Citation: WANG Dingpan, DONG Xiaohuan, HUANG Lingyong, WANG Xiaohua, LI Qingjun, JI Shunping. Information entropy uncertainty estimation based domain adaptation for land cover classification from multi-source remote sensing images[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220346

基于信息熵不确定性估计的域自适应多源遥感影像地表覆盖分类

Information entropy uncertainty estimation based domain adaptation for land cover classification from multi-source remote sensing images

  • 摘要: 在多源遥感影像地表覆盖分类中,引入域自适应方法能够对齐源域和目标域影像或其特征,从而提升深度学习模型的泛化能力,在智能遥感影像解译任务中具有重要意义。本文提出了一种基于信息熵不确定性估计的伪标签纠正方法,用于基于自训练的域自适应任务。其核心是提出一种熵不确定损失函数,用于跨源遥感影像之间的地表覆盖分类。首先,用含标签的源域影像训练语义分割模型,并对缺乏真实标签的目标域影像预测并生成伪标签。然后,用伪标签继续训练目标域影像,计算预测结果的信息熵,并将该信息熵作为伪标签的不确定性估计,以校正伪标签并再次进行目标域影像的自训练,得到适用于目标域数据集的地表覆盖分类模型权重。最后,在三套数据集,即WHU建筑物变化检测数据集、ISPRS 2D语义标注比赛数据集、武汉市2017年及2019年地表覆盖影像分类数据集上进行了跨域的多源遥感影像地表覆盖分类实验。结果显示:引入本方法将对现有语义分割网络的性能有显著提升;相比于传统自训练方法,本方法具有一定的提升,同时,本文方法也超越了最新的基于KL散度不确定性估计的方法。以上结果表明,本方法能够在原有预训练语义分割模型基础上进一步提升该模型对不含标签的目标域影像的分割能力,且不需要在原模型上增加额外的模块和参数。

     

    Abstract: Objectives: In the land cover classification study from multi-source remote sensing images, domain adaptation method can align images or extracted image features from source and target images, thus improves the generalization ability of deep learning models and plays an important role in intelligent remote sensing image interpretation. Methods: In this paper, a self-training domain adaptation method based on information entropy uncertainty estimation for pseudo label correction is proposed, its core is an entropy uncertainty loss function for land cover classification between cross source remote sensing images. Firstly, a land cover classification model is pretrained on the source domain training set with ground truth, and is applied on the target domain images without ground truth labels to generate pseudo labels. Then, the pseudo labels are used to further train the model, the information entropy of the prediction result is calculated and used as the uncertainty estimation of the pseudo labels to further correct the pseudo labels with self-training, so as to obtain weights of the classification model more suitable for the target domain dataset. Finally, a cross domain classification experiment was conducted on three data sets, namely, the WHU building change detection data set, the ISPRS 2D semantic annotation competition data set, and the Wuhan land cover classification data set. Results: Experimental results show that: (1) Our method improved the mIoU (mean Intersection over Union) and OA (Overall Accuracy) of semantic segmentation network by 0.3%-3.1% and 1.2%-4.5%, respectively. (2) Compared with the traditional self-training method, the proposed method can improve the mIoU and OA by 0.1%-1.5%. (3) Compared with the most recent uncertainty estimation method based on KL (Kullback-Leibler) divergence, the proposed method can improve the mIoU and OA about 0.6% in average. Conclusions: Our method can further improve the performance of a trained segmentation model for the target domain images without the requirement of target labels. There is also no need to introduce additional modules or parameters on the existing segmentation model.

     

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