CATSNet：基于Pol-TomoSAR数据的森林区域高度估计的上下文感知网络
CATSNet: a context-aware network for Height Estimation in a Forested Area based on Pol-TomoSAR data
AI总结：
CATSNet提出一种上下文感知的卷积神经网络，利用多基线Pol-TomoSAR数据的邻域图像块信息估计森林高度，在性能和泛化能力上超越现有逐像素方法。
摘要：
热带森林是全球碳循环的关键组成部分。随着即将开展的BIOMASS等星载任务计划用于监测森林，包括TropiSAR和AfriSAR活动在内的多个机载任务已成功启动并进行了实验。典型的合成孔径雷达层析成像（TomoSAR）方法涉及复杂模型，精度低且计算成本高。近年来，深度学习方法在TomoSAR框架中也受到关注，展现出令人关注的性能。最近，一种基于全连接层析神经网络（TSNN）的解决方案通过利用从TomoSAR数据导出的协方差矩阵的逐像素元素，证明了其在准确估计森林和地面高度方面的有效性。本研究则超越了逐像素方法，定义了一种名为CATSNet的上下文感知深度学习解决方案。采用卷积神经网络来利用基于图像块的信息，并从邻域中提取特征，而不是聚焦于单个像素。训练时将TomoSAR数据作为输入，将光探测与测距（LiDAR）值作为地面真值。实验结果表明，通过在不同极化模式下利用多基线（MB）TomoSAR数据中的上下文信息，该方法在性能和泛化能力方面均具有显著优势，超越了现有技术。
英文摘要：
Tropical forests are a key component of the global carbon cycle. With plans for upcoming space-borne missions like BIOMASS to monitor forestry, several airborne missions, including TropiSAR and AfriSAR campaigns, have been successfully launched and experimented. Typical Synthetic Aperture Radar Tomography (TomoSAR) methods involve complex models with low accuracy and high computation costs. In recent years, deep learning methods have also gained attention in the TomoSAR framework, showing interesting performance. Recently, a solution based on a fully connected Tomographic Neural Network (TSNN) has demonstrated its effectiveness in accurately estimating forest and ground heights by exploiting the pixel-wise elements of the covariance matrix derived from TomoSAR data. This work instead goes beyond the pixel-wise approach to define a context-aware deep learning-based solution named CATSNet. A convolutional neural network is considered to leverage patch-based information and extract features from a neighborhood rather than focus on a single pixel. The training is conducted by considering TomoSAR data as the input and Light Detection and Ranging (LiDAR) values as the ground truth. The experimental results show striking advantages in both performance and generalization ability by leveraging context information within Multiple Baselines (MB) TomoSAR data across different polarimetric modalities, surpassing existing techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
