Hyperspectral image change detection using two-branch Unet network with feature fusion

Change detection (CD) is the process of identifying differences in the state of an object or phenomenon by observing it at different times. CD is one of the earliest and most important applications of remote sensing technology. The hyperspectral image (HSI) of remote sensing satellite provides an important and unique data source for CD, but its high dimension, noise and limited data set make the task of CD very challenging. Traditional algorithms are no longer suitable for hyperspectral data processing. Recently, the success of deep convolutional neural networks (CNN) has widely spread across the whole field of computer vision for their powerful representation abilities. Therefore, this paper combines traditional algorithms and deep learning techniques to solve the CD task of hyperspectral remote sensing images. The proposed two-branch Unet network with feature fusion (Unet-ff) model in this paper uses neural networks to automatically extract features to achieve end-to-end change information detection. In order to improve the degree of automation in the application, we select the most effective results as the training sample for the neural network which obtained by various traditional algorithms, and use ground truth to evaluate the detection results. For the characteristics of hyperspectral data, we use effective dimensionality reduction methods and rich data amplification methods to improve the detection accuracy. Experimental results show that our method can achieve better results on the existing classical datasets.