Target Dictionary Construction-Based Sparse Representation Hyperspectral Target Detection Methods
Target Dictionary Construction-Based Sparse Representation Hyperspectral Target Detection Methods
复制标题
基于目标字典构建的稀疏表示高光谱目标检测方法
DOI：
10.1109/jstars.2019.2902430
复制
发表时间：
2019-03
影响因子：
5.5
通讯作者：
L. Zhang
中科院分区：
文献类型：
--
作者：
D. Zhu;B. Du;L. Zhang
高光谱图像具有高光谱分辨率，包含数百甚至数千个光谱波段，传递了丰富的光谱信息，为目标检测提供了独特的优势。基于线性混合模型（LMM）和稀疏性模型提出了许多经典的目标探测器。与LMM相比，稀疏型检测器在处理光谱变异性方面表现出更好的性能。尽管近年来基于稀疏性的模型取得了巨大的成功，但所有最先进的基于稀疏性的模型仍然存在一个问题：目标字典是通过从全局图像场景中选择的目标训练样本形成的。由于先验信息通常是从光谱库中获得的给定目标光谱，因此这种构造高光谱目标字典的方法是不正确的。此外，从全局图像场景中选择的目标训练样本通常不足，导致目标训练样本和背景训练样本在数据量上不平衡，导致检测模型变差。针对这些问题，本文构建了一种基于目标字典构造的方法，然后提出了基于构造目标字典稀疏性的目标检测模型和基于构造目标字典稀疏表示的二元假设模型，分别称为TDC-STD和TDC-SRBBH。这两种算法都只需要给定的目标频谱作为输入先验信息。利用给定的目标光谱，通过约束能量最小化进行预检测，选择输出值较大的像素作为目标训练样本，构建目标字典。在三个基准HSI数据集上对所提出的算法进行了测试，实验结果表明，与其他最先进的检测器相比，所提出的算法具有出色的检测性能。
Hyperspectral imagery (HSI) with a high spectral resolution contains hundreds and even thousands of spectral bands, and conveys abundant spectral information, which provides a unique advantage for target detection. A number of classical target detectors have been proposed based on the linear mixing model (LMM) and sparsity-based model. Compared with the LMM, sparsity-based detectors present a better performance on dealing with the spectral variability. Despite the great success of the sparsity-based model in recent years, one problem with all state-of-the-art sparsity-based models still exist: the target dictionary is formed via the target training samples that are selected from the global image scene. This is an improper way to construct target dictionary for hyperspectral target detection since the priori information is usually a given target spectrum obtained from a spectral library. Besides, target training samples selected from the global image scene are usually insufficient, which results in the problem that the target training samples and background training samples are unbalanced in the data volume, causing a deteriorated detection model. To tackle these problems, this paper constructs a target dictionary construction-based method, then proposes the constructed target dictionary-based sparsity-based target detection model and the constructed target dictionary-based sparse representation-based binary hypothesis model, which are called TDC-STD and TDC-SRBBH, respectively. Both of the proposed algorithms only need a given target spectrum as the input priori information. By using the given target spectrum for pre-detection via constrained energy minimization, we choose the pixels that have large output values as target training samples to construct the target dictionary. The proposed algorithms were tested on three benchmark HSI datasets and the experimental results show that the proposed algorithms demonstrate outstanding detection performances when compared with other state-of-the-art detectors.
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影响因子：
4.8
作者：
Yuxiang Zhang;Bo Du;Liangpei Zhang
通讯作者：
Yuxiang Zhang;Bo Du;Liangpei Zhang
DOI：
10.1109/tgrs.2006.873019
发表时间：
2006-08-01
影响因子：
8.2
作者：
Banerjee, Amit;Burlina, Philippe;Diehl, Chris
通讯作者：
Diehl, Chris
影响因子：
14.9
作者：
N. Nasrabadi
通讯作者：
N. Nasrabadi
DOI：
10.1109/whispers.2012.6874295
发表时间：
2012-06
期刊：
2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)
影响因子：
--
作者：
Wei Li;S. Prasad;J. Fowler;Q. Du
通讯作者：
Wei Li;S. Prasad;J. Fowler;Q. Du
影响因子：
8.2
作者：
Q. Du;Chein-I. Chang
通讯作者：
Q. Du;Chein-I. Chang