基于点云深度学习的对称结构空间目标相对位姿测量

针对对称结构空间目标相对位姿解算过程中点云误匹配带来的误差问题，提出一种基于点云深度学习的对称结构空间目标相对位姿测量方法。首先设计空间目标点云特征提取网络及关键点回归网络，将位姿测量问题转换为空间目标点云关键点回归问题，通过两个并行的回归网络分别输出空间目标平移向量和具有固定标签的目标点云三维边界框角点；其次利用具有连续稳定标签的角点求解目标姿态，可有效解决目标的对称结构导致的点云误配准问题；最后通过仿真数据集的实验表明，该方法相比于传统的点云配准方法有更高的准确率，能够精确求解具有对称结构的空间目标相对位姿。     

Comparison of residual and dense neural network approaches for building extraction from high-resolution aerial images

Applications including change detection, disaster management, and urban planning require precise building information, and therefore automatic building extraction has become a significant research topic. With the improvements in sensor and satellite technologies, more data has become available, and with the increased computational power, deep learning methods have emerged as successful tools. In this study, U-Net and FPN architectures using four different backbones (ResNet-50, ResNeXt-50, SE-ResNext-50, and DenseNet-121), and an Attention Residual U-Net approach were used for building extraction from high-resolution aerial images. Two publicly available datasets, Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset were used to train and test the models. According to the results, Attention Residual U-Net model has the highest F1 score with 0.8154, IoU score with 0.7102, and test accuracy with 94.51% on the Inria dataset. On the Massachusetts dataset, FPN Dense-Net-121 model has the highest F1 score with 0.7565 and IoU score with 0.6188, and Attention Residual U-Net model has the highest test accuracy with 92.43%. It has been observed that, FPN with DenseNet backbone can be a better choice when working with small size datasets. On the other hand, Attention Residual U-Net approach achieved higher success when a sufficiently large dataset is provided.     

“珠海一号”高光谱数据城市不透水面提取研究

不透水面是评估城市化进程和评价城市环境的重要参考指标，研究城市不透水面的空间覆盖程度对城市生态环境保护、宜居城市建设具有重要意义。本文以“珠海一号”高光谱影像为数据源、珠海市陆域为研究区域，选取支持向量机和随机森林机器学习算法，结合光谱特征，实现城市不透水面信息提取。结果表明：使用“珠海一号”高光谱数据提取不透水面可行性较强，且支持向量机算法更适用于“珠海一号”不透水面提取，总体精度和Kappa系数分别达到92.4%、0.78；基于“珠海一号”高光谱数据进行城市不透水面提取，可为城市规划建设、分析城市土地扩张、评估城市灾害风险提供可靠理论依据和数据支撑。