results: 该论文通过质量和量度分析表明，ISLAND方法在不同的云幕干扰和地面覆盖条件下具有良好的重建性能，并且具有高空间-时间分辨率。 authors还提供了20个美国城市的公共数据集，以便用于证明ISLAND方法的可行性和应用性。Abstract
Cloud occlusion is a common problem in the field of remote sensing, particularly for thermal infrared imaging. Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from Earth's surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable thermal images for downstream applications, making it impractical to perform intricate time-series analysis of land surface temperature (LST). In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator. Our approach uses thermal infrared images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler's first law of Geography, ISLAND predicts occluded brightness temperature and LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND thermal infrared and LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.
摘要
云层遮挡是远程感知领域中常见的问题，尤其是对于thermal infrared成像。远程感知thermal仪器装载在运行的卫星上，旨在实现频繁和高分辨率的地表观测;然而，云层会阻挡地表发射的长波辐射，使得抽取地表温度的热成像受到抑制，从而减少可用的热成像数据，使得无法进行复杂的时间序分析。在这篇文章中，我们介绍了一种新的云层遮挡除去方法，称为ISLAND（表示地表温度和辐射通过地域涂抹 interpolator）。我们的方法使用卫星8的热红外成像（分辨率30米，复杂周期16天）和NLCD地表覆盖数据。受到 Tobler's first law of Geography 的激发，ISLAND 预测云层遮挡的明亮温度和地表温度通过一系列的空间时间滤波来实现。我们的方法的一个关键特点是滤波是根据地表覆盖类型进行地域涂抹，这使得它在复杂的城市环境中具有优势。通过质量和量化分析，我们显示了ISLAND 在多种云层遮挡和地表覆盖条件下具有强健的重建性，并且具有高空间时间分辨率。我们提供了20个美国城市的前计算ISLAND 热红外成像和地表温度输出数据。通过多个案例研究，我们示出了ISLAND 可以开启许多高影响的城市和环境应用程序，覆盖整个北美大陆。
Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
methods: 提出使用物理规则embedded into the loss of physics-informed neural network（PINN）来学习 Bloch equation，并且通过这种方法来估算T2参数和生成physically synthetic data。
results: 在phantom和cardiac imaging中进行了实验，并得到了这种方法的潜在应用于量化MRI中的可能性。Abstract
Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in quantitative evaluation. The emerging deep learning approach provides fast and accurate parameter estimation but still encounters the lack of network interpretation and enough training data. Even with a large amount of training data, the mismatch between the training and target data may introduce errors. Here, we propose one way that solely relies on the target scanned data and does not need a pre-defined training database. We provide a proof-of-concept that embeds the physical rule of MRI, the Bloch equation, into the loss of physics-informed neural network (PINN). PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data. Experimental results are conducted on phantom and cardiac imaging to demonstrate its potential in quantitative MRI.
摘要