Masking Hyperspectral Imaging Data with Pretrained Models
This work addresses a domain-specific issue in hyperspectral imaging by improving computational efficiency and performance through masking, but it is incremental as it adapts existing models without fine-tuning.
The paper tackles the problem of unwanted background areas degrading hyperspectral data processing by proposing a masking pipeline using pretrained models like SAM and Grounding Dino for segmentation, applied to scenarios such as shredded plastics characterization, drill core scanning, and litter monitoring, with numerical evaluations provided.
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.