融合自适应窗口显著性检测和改进超像素分割的高光谱异常检测

Hyperspectral anomaly detection via combining adaptive window saliencydetection and improved superpixel segmentation

高光谱异常检测旨在识别与周围像素具有显著光谱差异的像素,由于不需要先验光谱信息的特点,其在军事和民用领域发挥重要价值。实现高光谱异常检测的一个重要手段是局部对比度计算,现有方法通常采用双窗口法进行计算,然而,窗口尺寸通常依据经验值进行手工设定,泛化能力不足。为了解决上述问题,本文提出了一种融合自适应窗口显著性检测和改进超像素分割的高光谱异常检测方法。该方法先利用对抗自编码网络对高光谱图像进行降维,以降低模型计算复杂度。其次,引入正交投影散度来改进超像素分割中的光谱距离度量方式,提升分割精度。然后,提出一种自适应窗口显著性检测算法来初步定位异常目标,该算法依据超像素分割结果来自适应确定双窗口,提高显著性检测的精度和泛化能力。最后,采用域变换递归滤波和阈值化操作对初始检测结果进行后处理,降低虚警率。消融实验表明,本文所提基于正交投影散度的改进超像素分割算法较基于传统光谱距离度量方式的算法性能更优,所提自适应窗口显著性检测算法的性能优于传统手工设定窗口尺寸的算法;与7种流行算法的主、客观对比实验表明,本文方法在总检测精度和异常—背景像素分离度上均优于流行算法。综上所述,本文所提改进超像素分割算法能提升现有高光谱图像超像素分割的效果,以此为基础所设计的自适应窗口显著性检测算法不仅能克服现有双窗口算法泛化能力不足的问题,还能获得优于流行算法的异常检测效果。

Hyperspectral anomaly detection is used to identify pixels with significant spectral contrast to their surrounding pixels.It plays a valuable role in military and civilian fields due to the characteristic that the priori spectral information is not required.The existing local contrast-based methods usually adopt dual rectangular window scheme for hyperspectral anomaly detection.However,they empirically set the size of dual window,which limits their generalization capability.A hyperspectral anomaly detection method via combining adaptive window saliency detection and improved superpixel segmentation is proposed in this study to address the abovementioned issue.An adversarial autoencoder is first introduced to reduce the dimension of the hyperspectral image for decreasing the computation complexity of the proposed method.Second,the dimension-reduced hyperspectral image is segmented by improved superpixel segmentation.The existing spectral distance measurements used in the superpixel segmentation are effective when the relationship between the spectral value and the intensity of each pixel is linear.However,this condition cannot be guaranteed in practical applications.The improved superpixel segmentation adopts the orthogonal projection divergence to measure the spectral distance for solving the aforementioned problem.Thereafter,an adaptive window-based saliency detection algorithm is proposed and used to obtain the initial detection results.Specifically,the size of the inner window is adaptively determined by the superpixels,which ensures that the pixels belonging to the same inner window are homogeneous.The outer window can be obtained by enlarging the inner window with fixed size.Finally,the domain transform recursive filter and thresholding operation are employed to optimize the initial detection results for reducing the false alarm rate.The comparisons between the orthogonal projection divergence and three common spectral distance measurements(Euclidean distance,spectral angular mapping,and spectral information divergence)in terms of AUC show that the orthogonal projection divergence-based method achieves the highest score on all five datasets.The comparisons between the adaptive window and traditional manual setting dual window in terms of AUC show that the adaptive window-based method achieves the highest score on all five datasets.Comprehensive comparisons between the proposed method and seven state-of-the-art methods on five public datasets are implemented to validate the overall performance of the proposed method.Specifically,the subjective comparisons show that the anomalous pixels detected by the proposed method are more precise and have stronger contrast to background regions.The objective comparisons demonstrate that the proposed method obtains the highest overall detection accuracy and offers the best separability between the anomalous and background pixels.Three conclusions can be derived from this study.First,the improved superpixel segmentation algorithm can enhance the segmentation results,and the proposed adaptive window scheme can increase the performance of saliency detection.Second,the proposed method has excellent detection accuracy,false alarm rate,and separability between the anomalous and background pixels.Finally,the overall performance of the proposed method is superior to that of state-of-the-art methods.