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Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information

机译：通过自适应联合稀疏表示和具有位置信息的多任务学习进行高光谱目标检测

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摘要

Target detection from hyperspectral images is an important problem but encounters a critical challenge of simultaneously reducing spectral redundancy and preserving the discriminative information. Recently, the joint sparse representation and multi-task learning (JSR-MTL) approach was proposed to address the challenge. However, it does not fully explore the prior class label information of the training samples and the difference between the target dictionary and background dictionary when constructing the model. Besides, there may exist estimation bias for the unknown coefficient matrix with the use of minimization which is usually inconsistent in variable selection. To address these problems, this paper proposes an adaptive joint sparse representation and multi-task learning detector with locality information (JSRMTL-ALI). The proposed method has the following capabilities: (1) it takes full advantage of the prior class label information to construct an adaptive joint sparse representation and multi-task learning model; (2) it explores the great difference between the target dictionary and background dictionary with different regularization strategies in order to better encode the task relatedness; (3) it applies locality information by imposing an iterative weight on the coefficient matrix in order to reduce the estimation bias. Extensive experiments were carried out on three hyperspectral images, and it was found that JSRMTL-ALI generally shows a better detection performance than the other target detection methods.
机译：从高光谱图像进行目标检测是一个重要的问题，但同时也面临着减少频谱冗余和保留区分性信息的严峻挑战。最近，提出了联合的稀疏表示和多任务学习（JSR-MTL）方法来应对这一挑战。但是，在构建模型时，它没有充分探索训练样本的先验类别标签信息以及目标字典和背景字典之间的差异。此外，使用最小化可能会存在未知系数矩阵的估计偏差，这通常与变量选择不一致。为了解决这些问题，本文提出了一种具有局部性信息的自适应联合稀疏表示和多任务学习检测器（JSRMTL-ALI）。所提出的方法具有以下功能：（1）充分利用先验类标签信息构建自适应的联合稀疏表示和多任务学习模型。 （2）探索了目标字典和背景字典在不同正则化策略之间的巨大差异，以更好地编码任务相关性; （3）通过在系数矩阵上施加迭代权重来应用位置信息，以减小估计偏差。在三个高光谱图像上进行了广泛的实验，发现JSRMTL-ALI通常显示出比其他目标检测方法更好的检测性能。

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