Onboard target detection of Hyperspectral Imagery (HSI) is widely adopted in the field of remote sensing. It requires high detection accuracy and low computational complexity for processing a large volume of HSI data. In this study, a Locally Preserving Discriminative Broad Learning (LPDBL) was introduced for target detection due to its simple, excellent generalization ability, and its competitive performance. The detection was done through spatial-spectral information, band selection, and estimation of the covariance matrix. The fisher discriminant method was used to reduce the dimension of HSI data. Weights was adjusted through manifold regularization in order to enhance the detection ability of the proposed method. To study the performance of the proposed LPDBL, experiment was conducted on two different datasets of HSI. The results revealed that the proposed method performed better and suitable for target detection. The LPDBL was implemented on Virtex-7 Field Programmable Gate Array (FPGA) board. Furthermore, the LPDBL technique was practically validated by two different techniques such as a broad learning system (BLS) and Automatic Target Detection in HSI (ATD-HSI). The result obtained from the FPGA was very close to the actual target position.

高光谱图像（HSI）的机载目标检测在遥感领域被广泛采用。处理大量HSI数据需要高检测精度和低计算复杂度。在这项研究中，由于其简单、出色的泛化能力和具有竞争力的性能，局部保留判别式广泛学习 (LPDBL) 被引入用于目标检测。检测是通过空间光谱信息、波段选择和协方差矩阵的估计来完成的。使用Fisher判别法对HSI数据进行降维。通过流形正则化调整权重以提高所提出方法的检测能力。为了研究所提出的 LPDBL 的性能，在 HSI 的两个不同数据集上进行了实验。结果表明，所提出的方法性能更好，适用于目标检测。LPDBL 在 Virtex-7 现场可编程门阵列 (FPGA) 板上实现。此外，LPDBL 技术通过两种不同的技术得到了实际验证，例如广泛的学习系统 (BLS) 和 HSI 中的自动目标检测 (ATD-HSI)。从 FPGA 获得的结果非常接近实际目标位置。