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.

Key words: change detection, convolutional neural networks, attention mechanism, image processing, information fusion