Fully supervised change detection methods require difficult to procure
pixel-level labels, while weakly supervised approaches can be trained with
image-level labels. However, most of these approaches require a combination of
changed and unchanged image pairs for training. Thus, these methods can not
directly be used for datasets where only changed image pairs are available. We
present W-CDNet, a novel weakly supervised change detection network that can be
trained with image-level semantic labels. Additionally, W-CDNet can be trained
with two different types of datasets, either containing changed image pairs
only or a mixture of changed and unchanged image pairs. Since we use
image-level semantic labels for training, we simultaneously create a change
mask and label the changed object for single-label images. W-CDNet employs a
W-shaped siamese U-net to extract feature maps from an image pair which then
get compared in order to create a raw change mask. The core part of our model,
the Change Segmentation and Classification (CSC) module, learns an accurate
change mask at a hidden layer by using a custom Remapping Block and then
segmenting the current input image with the change mask. The segmented image is
used to predict the image-level semantic label. The correct label can only be
predicted if the change mask actually marks relevant change. This forces the
model to learn an accurate change mask. We demonstrate the segmentation and
classification performance of our approach and achieve top results on AICD and
HRSCD, two public aerial imaging change detection datasets as well as on a Food
Waste change detection dataset. Our code is available at
this https URL .

本文提出了 W-CDNet，一种新型的基于弱监督学习方法的变化检测网络，可以通过基于图像级别的语义标签训练，既可以用于只包含变化图像对又可以用于包含变化和不变图像对的数据集；W-CDNet 使用两个 W-shaped Siamese U-Net 提取特征地图，然后比较这两个特征地图来创建原始变化遮罩，并利用 CSC 模块使用自定义重新映射块学习准确的变化遮罩并将当前输入图像分割成相应的标签。该方法在三个公开数据集上取得了当下最优的误差检测和分类性能。