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Received：13 March 2023，
Published：07 September 2023
移动端阅览
近年来，深度学习在多时相遥感影像变化检测任务中表现出巨大的潜力。充分的训练样本是深度学习技术能够有效挖掘遥感影像变化特征的重要前提，然而当前有限的公开标注数据集还不能满足实际应用中各种变化类型检测的需求。由于地表覆盖变化通常只占据少部分区域，能够获取的变化样本常常数量很少，且与不变化样本相比存在严重的不平衡问题。因此，如何在小样本与样本不平衡的情况下有效训练变化检测网络是急需突破的难题。相比变化检测样本，单时相地表覆盖分类样本的获取难度要低得多；在分类样本的支持下，充分训练的地表覆盖分类网络可为变化检测提供重要的先验特征。基于此，本文提出了一种基于分类后验概率空间的孪生Nested-UNet变化检测网络SNU-PS（Siamese Nested-UNet for change detection in Posterior Probability Space），通过结合两期地表覆盖分类后验概率信息，降低对变化检测样本的依赖。该方法首先利用地表覆盖分类样本训练高分辨率网络HRNet（High-Resolution Network），得到双时相影像的地物分类后验概率；然后将后验概率图像输入到孪生Nested-UNet变化检测网络SNU（Siamese Nested-UNet for change detection）中以获取变化检测结果。在SpaceNet7 和HRSCD数据集上测试的结果表明，SNU-PS能够充分利用地表覆盖的语义信息，在不同变化检测训练样本数量水平下，保持稳定的变化检测精度；相比分类后比较PCC（Post Classification Comparison）、基于后验概率空间的变化向量分析CVAPS（Change-vector analysis in posterior probability space）、以及各种类型变化检测网络（SNU、FC-EF、BIT、PCFN），具备更高与更稳定的变化检测精度，特别在样本数量不足时，优势更为明显。因此，本文提出的SNU-PS在小样本情形下的变化检测任务上具备更好的应用前景。
Deep learning has shown great potential in the change detection of multi-temporal remote sensing images in recent years. However
the annotated datasets
which are critically required in training change detection networks
is often limited in various change detection tasks in practical applications. As land cover change usually occupies only a small portion of an image
the number of changed samples is often very small
leading to a serious imbalance between changed and unchanged samples. Therefore
it is an urgent challenge to effectively training change detection networks with small and imbalanced change detection samples. Compared to the collection of change detection samples
it is much easier to obtain land cover classification samples at a single time. Based on the adequate land cover classification samples
a well-trained land cover segmentation network can provide important prior features for change detection.
Therefore
this paper proposes a method named as siamese Nested-UNet for change detection in posterior probability space (SNU-PS)
which aims to reduce the dependence on change detection samples by utilizing the posterior probability information of segmentation network. The method first trains a High-Resolution Network (HRNet) based on land cover classification samples to obtain the posterior probability of the bi-temporal image. Then
the posterior probability images are input into a siamese Nested-UNet for change detection(SNU) to obtain the change detection results. In order to simplify the network complexity and reduce the training difficulty
the training of semantic segmentation network and change detection network are carried out step by step without interactions in their training stages. As the posterior probability image already contains semantic information of land cover
the requirement of the change detection samples is reduced because the change detection network does not need to extract the features in the multi-spectral images.
The change detection experiments based on the SpaceNet7 and HRSCD datasets show that SNU-PS can well utilize the semantic information provided by the land cover segmentation network and maintain stable change detection accuracy when it was trained with different change detection sample sizes. Compared with Post Classification Comparison (PCC)
CVAPS (Change-vector analysis in posterior probability space)
and different change detection networks (FC-EF
BIT
PCFN
and SNU)
SNU-PS achieved higher accuracy and better stability
especially when the sample size is small. Unfortunately
all of the compared methods failed to identify the change type due to the extreme imbalanced samples of different change types.
SNU-PS method makes full use of the low-cost classification samples to train the semantic segmentation network
which helps to reduce the reliance on the change detection samples because the change detection network in SNU-PS does not undertake the feature exploration of multi-spectral images. Moreover
the semantic segmentation network and the change detection network are integrated with independent training process in SNU-PS
thus the integration of two networks does not increase the training difficulty and semantic segmentation network can be flexibly replaced with better network if available. In conclusion
the proposed SNU-PS maintains good performance under small sample size
thus has a good applicability in various change detection tasks.
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