城市空间发展易受地形所限,削山造地能克服土地资源稀缺,成为解决城市空间拓展最为直接的途径。该方法利用遥感技术快速准确获取削山造地范围信息,对区域生态环境科学评估和新城发展规划具有十分重要的意义。本文基于GEE遥感云计算平台...城市空间发展易受地形所限,削山造地能克服土地资源稀缺,成为解决城市空间拓展最为直接的途径。该方法利用遥感技术快速准确获取削山造地范围信息,对区域生态环境科学评估和新城发展规划具有十分重要的意义。本文基于GEE遥感云计算平台,利用Sentinel-1合成孔径雷达(synthetic aperture Rader,SAR)数据,采用组合升、降轨影像,在噪声滤除和多时相影像合成的基础上,计算削山造地前后后向散射强度的差值,并采用百分位阈值法结合样本数据确定阈值,提取研究区2017—2022年削山造地开挖区时空分布;然后联合SAR和光学数据的光谱特征、纹理特征和地形特征,在特征优化的基础上结合随机森林算法获取了2017—2022年逐年削山造地范围时空分布。研究结果表明:①提取的开挖区范围总体分类精度和Kappa系数分别达85%和0.83。②研究期间,发现2019年前开挖区主要集中在九州开发区、碧桂园和保利领秀山,2020年以后新增加了刘家沟、水源站等开挖区,开挖范围和强度逐渐增大。③2018年前造地规模较小,面积为2.655 km 2;2019年以后造地规模逐年增大,特别是2021年,其造地面积达12.607 km 2,占监测期间总造地面积的34.56%,2022年在原造地基础上开挖,因坡度和开挖量逐渐增大,造地面积仅2.686 km 2。本文构建的削山造地开挖区监测和造地范围提取方法可有效获取削山和造地范围快速监测与提取。展开更多
Three-dimensional （3-D） video applications, such as 3-D cinema, 3DTV, ana Free Viewpomt Video （FVV） are attracting more attention both from the industry and in the literature. High accuracy of depth video is a fun...Three-dimensional （3-D） video applications, such as 3-D cinema, 3DTV, ana Free Viewpomt Video （FVV） are attracting more attention both from the industry and in the literature. High accuracy of depth video is a fundamental prerequisite for most 3-D applications. However, accurate depth requires computationally intensive global optimization. This high computational complexity is one of the bottlenecks to applying depth generation to 3-D applications, especially for mobile networks since mobile terminals usually have limited computing ability. This paper presents a semi-global depth estimation algorithm based on temporal consis- tency, where the depth propagation is used to generate initial depth values for the computationally intensive global optimization. The accuracy of initial depth is improved by detecting and eliminating the depth propagation outliers before the global optimization. Integrating the initial values without outliers into the global optimization reduces the computational complexity while maintaining the depth accuracy. Tests demonstrate that the algorithm reduces the total computational time by 54%-65% while the quality of the virtual views is essentially equivalent to the benchmark.展开更多
文摘城市空间发展易受地形所限,削山造地能克服土地资源稀缺,成为解决城市空间拓展最为直接的途径。该方法利用遥感技术快速准确获取削山造地范围信息,对区域生态环境科学评估和新城发展规划具有十分重要的意义。本文基于GEE遥感云计算平台,利用Sentinel-1合成孔径雷达(synthetic aperture Rader,SAR)数据,采用组合升、降轨影像,在噪声滤除和多时相影像合成的基础上,计算削山造地前后后向散射强度的差值,并采用百分位阈值法结合样本数据确定阈值,提取研究区2017—2022年削山造地开挖区时空分布;然后联合SAR和光学数据的光谱特征、纹理特征和地形特征,在特征优化的基础上结合随机森林算法获取了2017—2022年逐年削山造地范围时空分布。研究结果表明:①提取的开挖区范围总体分类精度和Kappa系数分别达85%和0.83。②研究期间,发现2019年前开挖区主要集中在九州开发区、碧桂园和保利领秀山,2020年以后新增加了刘家沟、水源站等开挖区,开挖范围和强度逐渐增大。③2018年前造地规模较小,面积为2.655 km 2;2019年以后造地规模逐年增大,特别是2021年,其造地面积达12.607 km 2,占监测期间总造地面积的34.56%,2022年在原造地基础上开挖,因坡度和开挖量逐渐增大,造地面积仅2.686 km 2。本文构建的削山造地开挖区监测和造地范围提取方法可有效获取削山和造地范围快速监测与提取。
基金Supported by the National Key Basic Research and Development (973) Program of China (No.2010CB731800)
文摘Three-dimensional （3-D） video applications, such as 3-D cinema, 3DTV, ana Free Viewpomt Video （FVV） are attracting more attention both from the industry and in the literature. High accuracy of depth video is a fundamental prerequisite for most 3-D applications. However, accurate depth requires computationally intensive global optimization. This high computational complexity is one of the bottlenecks to applying depth generation to 3-D applications, especially for mobile networks since mobile terminals usually have limited computing ability. This paper presents a semi-global depth estimation algorithm based on temporal consis- tency, where the depth propagation is used to generate initial depth values for the computationally intensive global optimization. The accuracy of initial depth is improved by detecting and eliminating the depth propagation outliers before the global optimization. Integrating the initial values without outliers into the global optimization reduces the computational complexity while maintaining the depth accuracy. Tests demonstrate that the algorithm reduces the total computational time by 54%-65% while the quality of the virtual views is essentially equivalent to the benchmark.
