Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial–temporal variability is a challenging task.We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain.The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.     

Geosat／GM波形重跟踪反演中国沿海区域重力异常

利用Geosat/GM测高波形数据,在中国海域内(117°E～129°E,21°N～41°N)比较了阈值法、Beta5和改进阈值法三种重跟踪算法.分析表明,改进阚值法优于其他两种重跟踪方法.由改进阈值法的测高数据.分别采用最小二乘法(least sqlilare collocation,LSC)和逆Vening-Meinesz(IVM)计算了卫星测高重力异常,并与船测重力数据进行了比较.结果显示,LSC的精度优于IVM.与KMS02和Sandwell & Smith V15两个重力场模型相比较,本文结果在东海优于这两个模型,在台湾海域结果稍差,需要融合ERS-1等其他数据进一步提高精度.     

信息向量机、相关向量机和支持向量机在机载LiDAR点云数据分类中的应用

机载LiDAR点云数据分类技术是LiDAR数据后处理的关键步骤。信息向量机、相关向量机及支持向量机可以在LiDAR点云数据分类中发挥重要作用。本文将三种分类器应用到点云数据分类中,通过实验验证了它们在点云数据分类中的性能,总结了它们在点云数据分类任务中的应用潜力。