Stripes are artifacts in satellite images caused by various factors such as hardware defects. In some cases, these artifacts are introduced by some mitigating algorithms like Landsat SLC-off （Scan Line Corrector） ga...Stripes are artifacts in satellite images caused by various factors such as hardware defects. In some cases, these artifacts are introduced by some mitigating algorithms like Landsat SLC-off （Scan Line Corrector） gap-filling methods of LLHM （Local Linear Histogram Matching） and AWLHM （Adaptive Window Linear Histogram Matching）, which leave stripes as a byproduct. To improve Landsat SLC-off images with stripes,we propose an algorithm involving some hypothetical stripe-crossing stitch lines using the mean pixel value of the stitch lines.展开更多
Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program significantly benefits both the academic ...Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program significantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature（LST）. However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM ＋ and channel differences among sensors. Based on these investigations, the concerns are to：（1） show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors;（2） emphasize efforts which should be done for the quantitative applications of Landsat data; and（3） understand the potential challenges for the continuity of Landsat observation（i.e., thermal infrared） for global change monitoring, while several climate data record programs being in progress.展开更多
美国陆地卫星Landsat-7上搭载的专题扫描仪（ETM＋）上的扫描行校正器（SLC，scan line corrector）在2003年5月31日发生故障，导致Landsat-7影像出现坏行，难以正常使用。为了使剩余的78％的数据能够被利用，美国航空航天局（NASA）组...美国陆地卫星Landsat-7上搭载的专题扫描仪（ETM＋）上的扫描行校正器（SLC，scan line corrector）在2003年5月31日发生故障，导致Landsat-7影像出现坏行，难以正常使用。为了使剩余的78％的数据能够被利用，美国航空航天局（NASA）组织专家研究解决这一问题的方案，我们与NASA密切配合，探索了5种修复方法。经过对这5种方法的尝试与试验，其中自适应局部回归（ALR）算法修复后的图像完整没有明显的修复边界，达到理想的效果。本文介绍了ALR算法的基本原理、流程，并将该算法分别应用在美国和北京的缺行图像修复中，取得良好的效果。展开更多
文摘Stripes are artifacts in satellite images caused by various factors such as hardware defects. In some cases, these artifacts are introduced by some mitigating algorithms like Landsat SLC-off （Scan Line Corrector） gap-filling methods of LLHM （Local Linear Histogram Matching） and AWLHM （Adaptive Window Linear Histogram Matching）, which leave stripes as a byproduct. To improve Landsat SLC-off images with stripes,we propose an algorithm involving some hypothetical stripe-crossing stitch lines using the mean pixel value of the stitch lines.
基金supported by the National Key Research Program of China(No.2014CB953900)the National Natural Science Foundation of China(No.41375081)the Sun Yat-sen University“985 Project”Phase 3
文摘Since the launch of its first satellite in 1972, the Landsat program has operated continuously for more than forty years. A large data archive collected by the Landsat program significantly benefits both the academic community and society. Thermal imagery from Landsat sensors, provided with relatively high spatial resolution, is suitable for monitoring urban thermal environment. Growing use of Landsat data in monitoring urban thermal environment is demonstrated by increasing publications on this subject, especially over the last decade. Urban thermal environment is usually delineated by land surface temperature（LST）. However, the quantitative and accurate estimation of LST from Landsat data is still a challenge, especially for urban areas. This paper will discuss the main challenges for urban LST retrieval, including urban surface emissivity, atmospheric correction, radiometric calibration, and validation. In addition, we will discuss general challenges confronting the continuity of quantitative applications of Landsat observations. These challenges arise mainly from the scan line corrector failure of the Landsat 7 ETM ＋ and channel differences among sensors. Based on these investigations, the concerns are to：（1） show general users the limitation and possible uncertainty of the retrieved urban LST from the single thermal channel of Landsat sensors;（2） emphasize efforts which should be done for the quantitative applications of Landsat data; and（3） understand the potential challenges for the continuity of Landsat observation（i.e., thermal infrared） for global change monitoring, while several climate data record programs being in progress.
文摘美国陆地卫星Landsat-7上搭载的专题扫描仪（ETM＋）上的扫描行校正器（SLC，scan line corrector）在2003年5月31日发生故障，导致Landsat-7影像出现坏行，难以正常使用。为了使剩余的78％的数据能够被利用，美国航空航天局（NASA）组织专家研究解决这一问题的方案，我们与NASA密切配合，探索了5种修复方法。经过对这5种方法的尝试与试验，其中自适应局部回归（ALR）算法修复后的图像完整没有明显的修复边界，达到理想的效果。本文介绍了ALR算法的基本原理、流程，并将该算法分别应用在美国和北京的缺行图像修复中，取得良好的效果。
