results: 通过使用 Sentinel-1 卫星的 SAR 图像和 Landsat-8 卫星的多spectral图像进行实验验证，提出的方法可以实现优秀的视觉效果和数值性能，包括spectral distortion、相关系数、MSE、NIQE、BRISQUE 和 PIQE 等指标。Abstract
Considering that Coupled Dictionary Learning (CDL) method can obtain a reasonable linear mathematical relationship between resource images, we propose a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color fusion method. Firstly, the traditional Brovey transform is employed as a pre-processing method on the paired SAR and multispectral images. Then, CDL is used to capture the correlation between the pre-processed image pairs based on the dictionaries generated from the source images via enforced joint sparse coding. Afterward, the joint sparse representation in the pair of dictionaries is utilized to construct an image mask via calculating the reconstruction errors, and therefore generate the final fusion image. The experimental verification results of the SAR images from the Sentinel-1 satellite and the multispectral images from the Landsat-8 satellite show that the proposed method can achieve superior visual effects, and excellent quantitative performance in terms of spectral distortion, correlation coefficient, MSE, NIQE, BRISQUE, and PIQE.
摘要
基于coupled dictionary learning（CDL）方法，我们提出了一种新的Synthetic Aperture Radar（SAR）和多spectral pseudo-color融合方法。首先，我们使用传统的Brovey变换作为预处理方法，对paired SAR和多spectral图像进行预处理。然后，我们使用CDL来捕捉paired图像对的相关性，基于源图像生成的字典via强制联合稀热编码。接着，我们利用对的字典中的联合稀热表示来构建图像掩码，通过计算重建错误来生成最终融合图像。实验Result of SAR图像来自Sentinel-1卫星和多spectral图像来自Landsat-8卫星表明，提出的方法可以实现优秀的视觉效果，且在 spectral distortion、相关系数、MSE、NIQE、BRISQUE和PIQE等方面具有出色的量化表现。
Segment Anything Model for Pedestrian Infrastructure Inventory: Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data
results: 我们的发现表明，将来自移动LiDAR点云数据生成的街景图像与卫星图像数据结合使用，可以与SAM高效地创建可扩展的人行道基础设施清单，具有立即的利用价值，对于GIS专业人员、城市管理者、交通所有者和残疾人旅行者都具有重要意义。Abstract
In this paper, a Segment Anything Model (SAM)-based pedestrian infrastructure segmentation workflow is designed and optimized, which is capable of efficiently processing multi-sourced geospatial data including LiDAR data and satellite imagery data. We used an expanded definition of pedestrian infrastructure inventory which goes beyond the traditional transportation elements to include street furniture objects often omitted from the traditional definition. Our contributions lie in producing the necessary knowledge to answer the following two questions. First, which data representation can facilitate zero-shot segmentation of infrastructure objects with SAM? Second, how well does the SAM-based method perform on segmenting pedestrian infrastructure objects? Our findings indicate that street view images generated from mobile LiDAR point cloud data, when paired along with satellite imagery data, can work efficiently with SAM to create a scalable pedestrian infrastructure inventory approach with immediate benefits to GIS professionals, city managers, transportation owners, and walkers, especially those with travel-limiting disabilities.
摘要
在这篇论文中，我们设计了基于Segment Anything Model（SAM）的人行道基础设施分割工作流程，可以高效处理多源地ospatial数据，包括LiDAR数据和卫星图像数据。我们使用扩展的人行道基础设施清单定义，超出传统交通元素，包括通常被忽略的街 furniture对象。我们的贡献在于生成必要的知识，回答以下两个问题：首先，哪种数据表示可以通过SAM实现零批处理基础设施对象？第二，SAM基于方法如何在基础设施对象上进行分割？我们的发现表明，从移动LiDAR点云数据生成的街景图像，当与卫星图像数据结合使用时，可以高效地与SAM合作创建可扩展的人行道基础设施清单方法，具有立即的利益 дляGIS专业人员、城市管理者、交通所有人和残疾人，特别是那些受限的旅行者。