A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India)

Flood is one of the major recurrent natural disasters faced by the state of Bihar in north India. In the present study the authors assess the severity of flood hazard in Bihar, using 128 decadal historical satellite datasets acquired during different flood magnitudes during 1998 to 2010. The satellite‐based observations have been analysed in conjunction with the hydrological data, for assessing the frequency of inundation, severity of flood hazard and cropped land under flood hazard. This study assesses the spatial distribution of flooding and creation of systematic flood hazard database, which can be analysed from a spatial dimension in GIS. It is observed that about 24.56 lakh ha of the state's area and about 15.85 lakh ha of the cropped area are vulnerable to flood hazard. North Bihar is more vulnerable to flooding; 8 of the 10 areas identified as worst flood‐affected districts lie in this region.     

Unsupervised change detection in multitemporal SAR images using MRF models

An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.     

基于遥感方法的东平湖水域动态监测

以多时相遥感影像为数据源,通过重点分析最佳组合波段的选择和水体信息特征提取的图像处理方法,为遥感技术在水环境方面的研究提供一定的理论依据。同时,利用数字遥感技术实现随时间变化的水域动态监测和枯水期、丰水期的水域变化的动态监测,为水资源开发策略的制定提供科学的依据。     

多时相TM影像相对辐射校正研究

多时相影像对应波段中地物波谱“准”不变特征点(Pseudo-Invariant Features,PIFs)的选取是相对辐射校正的前提,采用目视解译选取主观性较强,在一定程度上影响了校正精度。利用绍兴试验区两时相TM影像,通过样本点间差异阈值,主成份分析和回归分析方法控制选取影像对应波段间的PIFs,使得校正样本点的选取具有客观性。在此基础上获取增益和偏移量并对两时相影像进行相对辐射校正,取得了较好的校正效果,从而有利于土地利用/覆盖变化监测。     

POPULATION AND RESOURCES IN TWO TYPES OF RURAL LANDSCAPE

Mørch, Henning: Population and resources in two types of rural landscape—Denmark 1860, 1900, and 1960. Geografisk Tidsskrift 80: 88–92. Copenhagen, June 1980.

An investigation on the basis of simple, linear regression of the relationship between size of population and physical resources in two types of landscape—moraine and outwash plain—Denmark 1860–1960.     

基于多时相影像的农业作物非参数与概率分类

本文提出了一种新的结合多光谱和变化检测技术的多时相卫星数据集分类方法。该方法以数理统计中的最近邻法为基础,其目标函数是使得正确分类的平均概率得到最优化,即把每个分类类别看成同等重要。该新算法被应用于一个农业作物分类的研究区域,并利用覆盖该区的不同季节的SPOT和LANDSAT TM多时像影像。结果表明,与单时像影像相比,使用五个不同季节的多时像影像可以充分地提高分类精度。为了说明该方法在大尺度范围内的效果,本文选取瑞典道拉河流域作为研究区。由于不同地物的分布高度重叠,不可能得到像元水平上满意的分类精度。这就需要引进一种新的概念：像元概率分类法。基于像元的概率向量可用于判别传统分类法的可靠性并测量单个像元的不确定性（熵）。概率分类法同时提供了不同地物的面积的无偏估计,无论所感兴趣的区域的大小。这已经在不同特性的耕地试验点进行了检验。     

Multitemporal terrestrial laser scanning point clouds for thaw subsidence observation at Arctic permafrost monitoring sites

This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice-rich permafrost-underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert-driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1-year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web-based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area-based assessment. Using the expert-driven quantification as reference, we validate the other methods, including in-situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert-driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in-situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark-based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert judgement for transparent long-term monitoring of permafrost subsidence. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd     

基于多时相遥感数据的海岸线自动提取方法

海岸线是海洋的重要组成部分,对于海域管理具有重要意义.本文根据岸线位置确定原理,即将多时相瞬时水边线上边界近似作为海岸线,提出了一种基于多时相水边线的海岸线自动提取方法,该方法应用区域生长与边缘检测相结合的方法提取瞬时水边线,然后采用海岸线自动判别算法进行海岸线的提取.首先计算归一化水体指数(normalized di...     

Misleading information from direct interpretation of Geometrically Incorrect aerial photographs

Remote sensing represents a powerful method to study the change of several phenomena over time. However, useful input data (for example, aerial photos) can produce misleading information if an inadequate geometric correction is applied. Among mathematical models used for this type of correction, orthorectification (differential correction) seems to be the only one that guarantees accurate results. However, many authors are still basing their results on incorrectly transformed images. This phenomenon is especially due to the incidence of several user-friendly tools and interfaces for the rectification of images. This paper tests both polynomial rectification and orthorectification. Aerial photography of 1954 of the same area was acquired and polynomial rectification and orthorectification were applied, using a 1998 orthophoto as a base. An unsupervised classification was performed by utilising the nearest neighbour method for the resampling of images. The area occupied by each class was calculated. A multitemporal analysis was then performed, considering the overlap between the 1954 orthophoto and the 1998 orthophoto as the base of reference. An overestimate up to 21% of class area was found. When multitemporal analysis was carried out, an overestimate up to 100% of class area change was found.     

基于YOLOv5算法的飞机类型光学遥感识别

飞机类型检测是遥感影像分析领域的研究热点,在机场监控和情报分析等应用中起着重要的作用。其中,深度学习方法作为遥感影像分析领域广泛应用的手段之一,在飞机类型检测任务中仍面临许多问题,如使用未公开的自制数据集、实验复现困难、无法验证泛化能力等。同时,光学遥感影像易受光照条件、云雨变化等因素影响,使检测任务更加困难。为了解决这些问题,本文首先利用MTARSI数据集对样本进行筛选,再结合Google Earth等开源方法收集飞机影像,采用随机旋转、改变亮度等方法构建新的飞机类型检测数据集。其次,采用YOLOv5作为基础网络框架,针对其多层卷积和池化操作可能会削弱或完全丢失飞机特征的问题,进行多尺度优化训练,有效检测飞机类型特征。最后,利用跨数据集验证模型的泛化能力。实验结果表明,本文方法能准确、有效地检测出光学遥感影像中的飞机的具体类型,具有较强的鲁棒性和泛化能力,跨数据集进行飞机类型检测正确率达到82.12%,可为智能化的飞机目标语义分析、星上应用等研究提供技术支撑。