TY - JOUR
T1 - Solar Panels Segmentation in Remote Sensing Data Using Segment Anything Models 2 and 2.1
AU - Rafaeli, Osher
AU - Svoray, Tal
AU - Blushtein-Livnon, Roni
AU - Nahlieli, Ariel
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - This paper provides insights on the effectiveness of the zero shot, prompt-based Segment Anything Model (SAM) and its updated versions, SAM 2 and SAM 2.1, along with the non-promptable conventional neural network (CNN), for segmenting solar panels in RGB aerial imagery. The study evaluates these models across diverse lighting conditions, spatial resolutions, and prompt strategies. SAM 2 showed slight improvements over SAM, while SAM 2.1 demonstrated notable improvements, particularly in sub-optimal lighting and low resolution conditions. SAM models, when prompted by user-defined boxes, outperformed CNN in all scenarios; in particular, user-box prompts were found crucial for achieving reasonable performance in low resolution data. Additionally, under high resolution, YOLOv9 automatic prompting outperformed user-points prompting by providing reliable prompts to SAM. Under low resolution, SAM 2.1 prompted by user points showed similar performance to SAM 2.1 prompted by YOLOv9, highlighting its zero shot improvements with a single click. In high resolution with optimal lighting imagery, Eff-UNet outperformed SAMs prompted by YOLOv9, while under sub-optimal lighting conditions, Eff-UNet, and SAM 2.1 prompted by YOLOv9, had similar performance. However, SAM is more resource-intensive, and despite improved inference time of SAM 2.1, Eff-UNet is more suitable for automatic segmentation in high resolution data. This research details strengths and limitations of each model and outlines the robustness of user-prompted image segmentation models.
AB - This paper provides insights on the effectiveness of the zero shot, prompt-based Segment Anything Model (SAM) and its updated versions, SAM 2 and SAM 2.1, along with the non-promptable conventional neural network (CNN), for segmenting solar panels in RGB aerial imagery. The study evaluates these models across diverse lighting conditions, spatial resolutions, and prompt strategies. SAM 2 showed slight improvements over SAM, while SAM 2.1 demonstrated notable improvements, particularly in sub-optimal lighting and low resolution conditions. SAM models, when prompted by user-defined boxes, outperformed CNN in all scenarios; in particular, user-box prompts were found crucial for achieving reasonable performance in low resolution data. Additionally, under high resolution, YOLOv9 automatic prompting outperformed user-points prompting by providing reliable prompts to SAM. Under low resolution, SAM 2.1 prompted by user points showed similar performance to SAM 2.1 prompted by YOLOv9, highlighting its zero shot improvements with a single click. In high resolution with optimal lighting imagery, Eff-UNet outperformed SAMs prompted by YOLOv9, while under sub-optimal lighting conditions, Eff-UNet, and SAM 2.1 prompted by YOLOv9, had similar performance. However, SAM is more resource-intensive, and despite improved inference time of SAM 2.1, Eff-UNet is more suitable for automatic segmentation in high resolution data. This research details strengths and limitations of each model and outlines the robustness of user-prompted image segmentation models.
KW - Remote sensing
KW - SAM 2
KW - SAM 2.1
KW - Solar panels
KW - Transfer learning
KW - YOLO
UR - http://www.scopus.com/inward/record.url?scp=85213238275&partnerID=8YFLogxK
U2 - 10.1109/LGRS.2024.3522183
DO - 10.1109/LGRS.2024.3522183
M3 - Article
AN - SCOPUS:85213238275
SN - 1545-598X
JO - IEEE Geoscience and Remote Sensing Letters
JF - IEEE Geoscience and Remote Sensing Letters
ER -