基于深度学习网络的遥感图像异常检测方法研究

An Anomaly Detection Method for Remote Sensing Image Based onDeep Learning Network

针对现实世界中异常图像数据稀少的数据不均衡问题,构建了一个高性能的异常检测模型.仅使用正常训练数据和小部分仿真异常数据,构建了两阶段框架的异常检测模型.通过对正常数据和模拟生成的异常数据进行分类训练,得到提取特征的ResNet-18编码器模型,通过高斯密度估计对正常数据的特征建模,构建异常图像的单分类器.Grad-CAM扩展了模型,使得异常检测模型可以在没有标签的情况下定位异常区域.通过仿真异常检测数据集上进行的实验证明,提出的算法能够检测现实世界遥感图像中人类肉眼难以发现的异常样本,并给出定位结果.

A high-performance anomaly detection model has been constructed to address the problem of sparse anomalous image data in the real world.A two-stage framework anomaly detection model is built using only normal training data and a small amount of synthetic anomaly sample.First,a ResNet-18 encoder model is trained to extract representation by the pretext of classifying normal data and synthetic anomaly data.Then,a single classifier for anomaly images is built through modelling the distribution of normal data representations using Gaussian density estimation.GradCAM is applied to extend the model,enabling the anomaly detection model to locate anomaly regions without labels.Finally,experiments are conducted on a simulated anomaly detection dataset using real-world images,demonstrating that the proposed algorithm can detect anomaly and provide location results in remote sensing images that are even difficult to recognize with human eyes.