ABSTRACT

Due to the development of hyperspectral image (HSI) technology, the high-resolution hyperspectral image (HRHSI) in remote sensing is becoming widely used. Compared to traditional HSI, HRHSI has extremely high resolution in both spatial and spectral domains. It contains more texture and spectral information than the low-resolution HSI (LRHSI), which can improve the target detection performance of HSI. However, the majority of the existing automatic target detection methods are only applicable to LRHSI. Therefore, this paper brings forward to a spatial-spectral feature-based target detection framework for HRHSI. First, a two-channel residual network is proposed, which aims to learn jointly spatial-spectral features from the spectral domain and spatial domain of HRHSI. Second, a spatial-spectral feature space is constructed to describe the distribution of the spatial-spectral feature of HRHSI, which can overcome the limitation of the number of training samples. A combined loss function is used to minimize within-class differences and maximize between-class distance in the spatial-spectral feature space. Finally, the detection map is received in the spatial-spectral feature space by calculating the Mahalanobis Distance and analysing the credibility of the target. The experimental results show that our algorithm achieves better target detection accuracy when the number of training samples is limited.

摘要

由于高光谱图像（HSI）技术的发展，高分辨率高光谱图像（HRHSI）在遥感中得到了广泛的应用。与传统的 HSI 相比，HRHSI 在空间和光谱域都具有极高的分辨率。它比低分辨率 HSI（LRHSI）包含更多的纹理和光谱信息，可以提高 HSI 的目标检测性能。然而，现有的大多数自动目标检测方法仅适用于LRHSI。因此，本文提出了一种基于空间光谱特征的 HRHSI 目标检测框架。首先，提出了一种双通道残差网络，旨在从 HRHSI 的谱域和空间域联合学习空间谱特征。第二，构建空间谱特征空间来描述HRHSI空间谱特征的分布，克服训练样本数量的限制。组合损失函数用于最小化空间光谱特征空间中的类内差异并最大化类间距离。最后，通过计算马氏距离和分析目标的可信度，在空间光谱特征空间中接收检测图。实验结果表明，在训练样本数量有限的情况下，我们的算法实现了更好的目标检测精度。组合损失函数用于最小化空间光谱特征空间中的类内差异并最大化类间距离。最后，通过计算马氏距离和分析目标的可信度，在空间光谱特征空间中接收检测图。实验结果表明，在训练样本数量有限的情况下，我们的算法实现了更好的目标检测精度。组合损失函数用于最小化空间光谱特征空间中的类内差异并最大化类间距离。最后，通过计算马氏距离和分析目标的可信度，在空间光谱特征空间中接收检测图。实验结果表明，在训练样本数量有限的情况下，我们的算法实现了更好的目标检测精度。