This paper considers image change detection with only a small number of
samples, which is a significant problem in terms of a few annotations
available. A major impediment of image change detection task is the lack of
large annotated datasets covering a wide variety of scenes. Change detection
models trained on insufficient datasets have shown poor generalization
capability. To address the poor generalization issue, we propose using simple
image processing methods for generating synthetic but informative datasets, and
design an early fusion network based on object detection which could outperform
the siamese neural network. Our key insight is that the synthetic data enables
the trained model to have good generalization ability for various scenarios. We
compare the model trained on the synthetic data with that on the real-world
data captured from a challenging dataset, CDNet, using six different test sets.
The results demonstrate that the synthetic data is informative enough to
achieve higher generalization ability than the insufficient real-world data.
Besides, the experiment shows that utilizing a few (often tens of) samples to
fine-tune the model trained on the synthetic data will achieve excellent
results.

这篇论文主要讨论了利用少量样本进行图像变化检测的问题，并通过使用简单的图像处理方法生成合成但具有信息量的数据集并设计了基于目标检测的早期融合网络来解决数据集不足导致的泛化能力差的问题。研究结果表明，使用合成数据训练的模型具有更好的泛化能力，且在此基础上利用少量样本进行微调能够获得优秀的结果。

仅有少量样本的图像变化检测

Image change detection with only a few samples

Recently, synthetic aperture radar (SAR) image change detection has become an
interesting yet challenging direction due to the presence of speckle noise.
Although both traditional and modern learning-driven methods attempted to
overcome this challenge, deep convolutional neural networks (DCNNs)-based
methods are still hindered by the lack of interpretability and the requirement
of large computation power. To overcome this drawback, wavelet scattering
network (WSN) and Fourier scattering network (FSN) are proposed. Combining
respective merits of WSN and FSN, we propose Stockwell scattering network (SSN)
based on Stockwell transform which is widely applied against noisy signals and
shows advantageous characteristics in speckle reduction. The proposed SSN
provides noise-resilient feature representation and obtains state-of-art
performance in SAR image change detection as well as high computational
efficiency. Experimental results on three real SAR image datasets demonstrate
the effectiveness of the proposed method.

本研究利用 Stockwell 变换提出了 Stockwell 散射网络（SSN）作为一种合成孔径雷达（SAR）图像变化检测的方法，该方法结合了 WS​​N 和 FSN 的优点，并在 SAR 图像变化检测方面取得了最新的技术成果。实验结果表明，所提出的方法在三个真实的 SAR 图像数据集上表现出了很好的性能。

基于 Stockwell 散射网络的 SAR 图像变化检测

SSN: Stockwell Scattering Network for SAR Image Change Detection

A modified version of MRFFCM (Markov Random Field Fuzzy C means) based SAR
(Synthetic aperture Radar) image change detection method is proposed in this
paper. It involves three steps: Difference Image (DI) generation by using
Gauss-log ratio operator, speckle noise reduction by SRAD (Speckle Reducing
Anisotropic Diffusion), and the detection of changed regions by using MRFFCM.
The proposed method is compared with existing methods such as FCM and MRFFCM
using simulated and real SAR images. The measures used for evaluation includes
Overall Error (OE), Percentage Correct Classification (PCC), Kappa Coefficient
(KC), Root Mean Square Error (RMSE), and Peak Signal to Noise Ratio (PSNR). The
results show that the proposed method is better compared to FCM and MRFFCM
based change detection method.

本文提出了一种改进的基于 MRFFCM 的 SAR 图像变化检测方法，包括 DI 生成，SRAD 降噪和 MRFFCM 变化检测三个步骤，并使用 OE，PCC，KC，RMSE 和 PSNR 等评估指标与 FCM 和 MRFFCM 进行比较，结果表明提出的方法优于 FCM 和 MRFFCM 基于变化检测方法。