Spectral-frequency domain attribute pattern fusion for hyperspectral image change detection
HyperSpectral Imagery(HSI)is a three-dimensional cube data that combines spatial imagery and spectral information,which introduces increased conveniences to the accurate interpretation of observation information of ground coverings.However,high-dimensional nonlinear data processing for the HSI Change Detection(HSI-CD)task encounters challenges.Therefore,an HSI-CD method based on Spectral-Frequency Domain Attribute Pattern Fusion(SFDAPF)is introduced to gradually quantify the spectral representation of pixel attribute patterns.Specifically,a Saliency Enhancement(SE)strategy for pixel attribute patterns based on Fourier transform theory is developed to improve the separability between pixel attribute patterns in the current work.The proposed SFDAPF method comprises four components as follows.First,a gradient correlation-based spectral absolute distance(GCASD)is designed in this paper.Therefore,the attribute patterns of pixel pairs in bitemporal HSI can be gradually quantified from the aspect of spectral information representation.Then,an SE strategy of attribute patterns of pixel pairs is proposed in accordance with Fourier transform theory,which improves the separability of attribute patterns of changing and non-changing pixel pairs in terms of global spatial information utilization.Next,the saliency level and GCASD per pixel are fused to obtain the comprehensive discrimination value of change detection.Finally,the binarization results of the bitemporal HSI-CD are obtained in accordance with the false alarm threshold.The proposed SFDAPF method is applied to two open-source bitemporal HSI datasets(i.e.,River and Farmland datasets).Experimental results show that the proposed SFDAPF method can outperform the traditional and state-of-the-art HSI-CD methods.For the River dataset,compared with the traditional methods,the SFDAPF method in this paper introduces the local context information of the pixel in the calculation stage of the GCASD and adopts the global SE strategy,which is effective in reducing false alarms.Compared with the state-of-the-art methods,the SFDAPF method in this paper achieves the highest accuracy for most of the performance evaluation indicators.For the Farmland dataset,the AA,Kappa,F1,IoU,and OA indicators of the SFDAPF method in this paper have reached the highest accuracy,which is 0.01985,0.05653,0.01474,0.02798,and 0.02187 higher than the second highest accuracy.In addition,the OAu(0.97500)and OAc(0.96766)indicators of the SFDAPF method did not achieve the highest accuracy.However,they were only 0.00673 and 0.01237 lower than the highest accuracy,which can be called slightly lower than the highest accuracy.Therefore,the experiments verified the effectiveness of the proposed SFDAPF method in the HSI-CD task.The proposed SFDAPF method generally considers the representation of spectral information and the utilization of neighborhood spatial information,thus promoting the overall accuracy of HSI-CD.However,the proposed SFDAPF method only considers the single-window eight-connected neighborhood in the spectral characterization stage and the magnitude features represented in the frequency domain.Therefore,future research work should further explore the contribution of dual-window spectral information representation and phase information of frequency domain representation to HSI-CD task.
万方数据
维普
