西安电子科技大学学报（自然科学版）2025，Vol.52Issue(1)：105-116,12.DOI:10.19665/j.issn1001-2400.20241011
联合多尺度高低频信息融合的变化检测方法
Change detection method based on multi-scale and multi-resolution information fusion
摘要
Abstract
Hyperspectral image change detection has emerged as a crucial technique to identify the change of ground objects in natural scenes by incorporating abundant spectral information in hyperspectral images taken in different phases in the same area.With the thrive of deep learning,hyperspectral image change detection methods can be mainly categorized into the convolutional neural network(CNN)-based and Transformer-based method.The CNN-based methods typically adopt convolutional kernels for feature extraction,which hold the characteristics of a small receptive field and focus on local information on the image,leading to the lack of sufficient modeling of the global information.The Transformer-based methods concentrate mainly on establishing global image dependencies without taking effective local information into consideration,leading to missed or false detections in change detection tasks.To address these limitations,this paper proposes a change detection method based on multi-scale and multi-resolution information fusion.Concretely,a pyramid multi-scale high and low-frequency information extraction network is first designed to capture high-frequency details and the low-frequency content,which attach their attention on the boundary region and background region respectively at different scales of multi-temporal hyperspectral images.High-frequency information is extracted through a residual convolutional network to model local features at different scales,while low-frequency information is captured through an attention-based network to model global features.Furthermore,a dual-time-phase differential classification decision network is proposed to enhance feature extraction by adaptively learning the classification weight coefficients of each branch and generating the final weighted prediction results.The qualitative and quantitative results on three real hyperspectral datasets show that the proposed method not only showcase a superior performance on the change detection task,but also achieves a more stable and higher classification accuracy.关键词
变化检测/卷积神经网络/注意力机制/图像处理/信息融合Key words
change detection/convolutional neural networks/attention mechanism/image processing/information fusion分类
计算机与自动化引用本文复制引用
曲家慧,贺杰,董文倩,李云松,张同振,杨宇菲..联合多尺度高低频信息融合的变化检测方法[J].西安电子科技大学学报（自然科学版）,2025,52(1):105-116,12.基金项目
国家自然科学基金(62201423,62471359) （62201423,62471359）
