The presence of undesired background areas associated with potential noise and unknown spectral characteristics degrades the performance of hyperspectral data processing. Masking out unwanted regions is key to addressing this issue. Processing only regions of interest yields notable improvements in terms of computational costs, required memory, and overall performance. The proposed processing pipeline encompasses two fundamental parts: regions of interest mask generation, followed by the application of hyperspectral data processing techniques solely on the newly masked hyperspectral cube. The novelty of our work lies in the methodology adopted for the preliminary image segmentation. We employ the Segment Anything Model (SAM) to extract all objects within the dataset, and subsequently refine the segments with a zero-shot Grounding Dino object detector, followed by intersection and exclusion filtering steps, without the need for fine-tuning or retraining. To illustrate the efficacy of the masking procedure, the proposed method is deployed on three challenging applications scenarios that demand accurate masking; shredded plastics characterization, drill core scanning, and litter monitoring. The numerical evaluation of the proposed masking method on the three applications is provided along with the used hyperparameters. The scripts for the method will be available at https://github.com/hifexplo/Masking.

翻译：高光谱数据处理过程中，由潜在噪声及未知光谱特征引起的非目标背景区域会降低处理性能。掩膜剔除非目标区域是解决该问题的关键。仅处理感兴趣区域可在计算成本、内存需求和整体性能方面带来显著提升。本文提出的处理流程包含两个核心部分：首先生成感兴趣区域掩膜，随后仅对掩膜处理后的高光谱立方体应用高光谱数据处理技术。本研究的创新之处在于所采用的图像分割方法。我们运用Segment Anything Model（SAM）提取数据集中所有目标，再通过零样本Grounding Dino目标检测器对分割区域进行细化，结合交集与排除过滤步骤进行处理，无需进行微调或重新训练。为验证掩膜流程的有效性，本文将该方法应用于三个需要精准掩膜的挑战性场景：碎塑料特性分析、岩芯扫描和垃圾监测。本文给出了三种应用场景下所提掩膜方法的数值评估结果及超参数设置。该方法的相关脚本将发布于https://github.com/hifexplo/Masking。