影响传统高光谱目标检测精度的因素主要有两个方面:一是高光谱图像固有的光谱畸变造成的图像噪声;另一个是传统方法在处理目标检测和利用空间信息时,会将异质区域与同质区域同等对待,然而异质区域却包含着不同的物质和光谱特征。为了解决以上问题,提出了一种将空间自适应模型与稀疏表示结合起来对高光谱图像目标进行检测的方法。首先,在重建信号时利用噪声的稀疏表示特性,最大限度地提取噪声中包含的有用信息,以确保重建信号的特征更加丰富,并接近源信号;其次,提出了一种空间自适应权重模型,并用它来检测中心像素点同周围邻域不同像素的相似度,最大限度地利用空间邻域像素之间的关系。最终的实验结果表明,所提方法比传统的稀疏表示高光谱目标检测方法更具稳健性。

There exist two factors influencing the accuracy of conventional hyperspectral target detection. One is the inherent image noises induced by spectral distortion, and the other is the equal contributions of all adjacent pixels in the heterogeneous region. However, in fact the heterogeneity implies that the pixels are composed of different materials and possess different spectral characteristics. To address these problems, we propose a hyperspectral target detection method by the combination of spatially adaptive model and sparse representation. The noise sparse representation is utilized to reconstruct an accurate signal, in which the useful information in noises is extracted as possible to make the reconstructed signal be full of more features and be close to the original signal. In addition, a spatially adaptive weighted model is proposed to detect the similarity between central pixel and neighboring pixels, and to make full use of the relationship among neighboring pixels. The final experimental results show that the proposed method possesses a strong robustness compared with the conventional hyperspectral target detection methods.