高分影像辅助下的航空高光谱严密几何检校方法

 惯性测量单元(IMU)与传感器视准轴的偏心角和偏心矢量是造成航空线阵列高光谱数据几何校正误差的主要原因之一。在分析偏心角与偏心矢量误差来源之后提出该误差由IMU主轴与传感器主轴的角度偏差、测区固定偏差、GPS中心与传感器投影中心相对偏差组成,在此基础上建立了较为严密的检校模型。针对模型解算时需要大量高精度控制点的问题,提出了一种高分影像辅助下的亚像元精度控制点自动提取方法。通过多地区、多传感器高光谱航测实验表明,亚像元精度控制点能有效提高模型解算精度。新检校模型可获得亚像元校正精度,推扫式传感器——应用型机载成像光谱仪(AISA)建模中误差约为0.39个像元,摆扫式传感器——实用型模块化成像光谱仪(OMIS)建模中误差约为0.23个像元,校正后的影像可直接进行拼接。     

斯特林制冷机真空环境模拟试验

文章针对空间红外遥感器应用特点,进行了斯特林制冷机的模拟真空环境试验。试验结果表明:温度环境和导热条件是影响斯特林制冷机空间应用的重要因素。     

基于TC35i和C8051F020的远程数据采集系统

本文提出了一种用混合信号系统级单片机C8051F020控制GSM网络引擎TC35i实现远程数据采集的方案。在描述了远程数据采集系统原理、构成和功能的基础上，对无线通讯模块及硬件和软件设计部分也进行了详细介绍。     

Cost-driven design of small satellite remote sensing systems*

Earth remote sensing (alongside communications) is one of the key application of Earth-orbiting satellites. Civilian satellites in the LANDSAT and SPOT series provide Earth images which have been used for a vast spectrum of applications in agriculture, meteorology, hydrology, urban planning and geology, to name but a few. In the defence sector, satellite remote sensing systems are a critical tool in strategic and tactical planning – for the countries which can afford them. To date, remote sensing satellites have fallen into one of these two categories: military missions driven by the requirement for very high resolution and orbital agility; and multipurpose civil satellites using general purpose sensors to serve a diverse community of end users. For military-style missions, the drive to high resolution sets the requirements for optics, attitude control and downlink data bandwidth. For civil missions, the requirement to satisfy multiple, diverse user applications forces compromises on spectral band and orbit selection. Although there are exceptions, many small satellite remote sensing missions carry on in this tradition, concentrating on ultra high resolution products for multiple user communities. This results in satellites costing on the order of US $100 M, not optimised for any particular application. This paper explores an alternative path to satellite remote sensing, aiming simultaneously to reduce cost and to optimise imaging products for specific applications. By decreasing the cost of the remote sensing satellite system to a critical point, it becomes appropriate to optimise the sensor's spectral and temporal characteristics to fit the requirements of a small, specialised user base. The critical engineering trade-off faced in a cost driven mission is how to reduce mission cost while still delivering a useful product to the selected user. At the Surrey Space Centre, we have pursued an engineering path using two dimensional CCD array sensors, commercial off-the-shelf lenses and gravity-gradient stabilised microsatellites. In spite of the inherent limitations of such systems, recent successes with the Thai Microsatellite Company's Thai-Phutt satellite show that a system costing in the region of US $3 million, can approach the spectral and spatial characteristics of LANDSAT. Surrey's UoSAT-12 minisatellite (to be launched April, 1999) will further develop this cost-driven approach to provide 10 m panchromatic resolution and 30 m multi-spectral resolution. This paper describes the Thai-Phutt and UoSAT-12 imaging systems, explaining the engineering methods and trade-offs. Although Surrey is presently the only centre presently pursuing such implementations, our paper shows that they deserve wider consideration.     

A modern robust approach to remotely estimate chlorophyll in coastal and inland zones

The chlorophyll concentration of a water body is an important proxy for representing the phytoplankton biomass. Its estimation from multi or hyper-spectral remote sensing data in natural waters is generally achieved by using (i) the waveband ratioing in two or more bands in the blue-green or (ii) by using a combination of the radiance peak position and magnitude in the red-near-infrared (NIR) spectrum. The blue-green ratio algorithms have been extensively used with satellite ocean color data to investigate chlorophyll distributions in open ocean and clear waters and the application of red-NIR algorithms is often restricted to turbid productive water bodies. These issues present the greatest obstacles to our ability to formulate a modern robust method suitable for quantitative assessments of the chlorophyll concentration in a diverse range of water types. The present study is focused to investigate the normalized water-leaving radiance spectra in the visible and NIR region and propose a robust algorithm (Generalized ABI, GABI algorithm) for chlorophyll concentration retrieval based on Algal Bloom index (ABI) which separates phytoplankton signals from other constituents in the water column. The GABI algorithm is validated using independent in-situ data from various regional to global waters and its performance is further evaluated by comparison with the blue-green waveband ratios and red-NIR algorithms. The results revealed that GABI yields significantly more accurate chlorophyll concentrations (with uncertainties less than 13.5%) and remains more stable in different waters types when compared with the blue-green waveband ratios and red-NIR algorithms. The performance of GABI is further demonstrated using HICO images from nearshore turbid productive waters and MERIS and MODIS-Aqua images from coastal and offshore waters of the Arabian Sea, Bay of Bengal and East China Sea.     

TopPixelLoss:类别不均衡的遥感影像语义分割损失函数

针对遥感影像中类别不均衡的小目标分割效果不理想的问题，提出了一种类别不均衡小目标二分类分割的损失函数——TopPixelLoss损失函数。首先计算出每个像素的交叉熵，然后将所有像素的交叉熵按从大到小进行排序，随后确定一个K值作为阈值，筛选出前K个交叉熵最大的像素，最后对于筛选出的K个像素交叉熵取平均，做为损失值。在ISPRS 提供的 Vaihingen 数据集上，使用PSPNet网络与普通交叉熵、FocalLoss、TopPixelLoss三种损失函数分别对车辆进行二分类分割试验。结果表明，不同的K值，使用TopPixelLoss损失函数的平均交并比（MIoU）、F1-score、准确度（ACC）都最高;当K值为5×104时效果最佳，MIoU、F1-score、ACC分别比FocalLoss提高了3.0%、5.0%、0.1%。TopPixelLoss损失函数是一种针对类别不均衡分割非常有效的损失函数     

Degradation monitoring in Silvo-pastoral systems: A case study of the Mediterranean region of Turkey

To identify policies that will promote positive effects and mitigate negative ones of grazing is a major challenge in the Silvo-pastoral system. This paper presents the role of examining land-cover change trajectories by remote sensing imagery in grazing policy monitoring. The study was conducted for Duzlercami forest ecosystem located in the Mediterranean geographical region of Turkey and administrated by the General Directorate of Forestry (GDF) of the Ministry of Forestry and Water Affairs. Time series land-cover datasets from Landsat images between 1988 and 2016 were collected and classified. To link the conversions among trajectories and grazing policy, class level landscape metrics derived from the classified images were used. To validate the approach, yearly grazing-plans managed by GDF and populations of livestock were used. Results of this research have indicated that even though there is a yearly grazing plan, overgrazing can happen on the pilot site, and it can be easily identified by the destruction of woody vegetation. The notable correlation (r²?=?0.89) between degraded woody vegetation and cattle population has occurred in the last 30?years in the landscape, and Landsat imagery can effectively support the grazing policy mapping and monitoring.     

基于多尺度 3D-2D卷积神经网络的高光谱图像分类

设计提出了 1种针对高光谱图像分类任务的 3D-MSCNN模型。在 PCA降维的基础上,利用 3D空谱特征提 取网络和 2D多尺度特征提取网络实现高光谱图像特征提取,充分发挥高光谱图像空谱信息价值,增强对不同尺度 地表覆盖的表达能力。最后,利用 Softmax分类损失函数实现高光谱图像分类任务。实验结果表明,本文算法在 In. dian Pines和 Pavia University数据集上都取得了较好的分类效果。与 CD-CNN、3D-CNN、SS-Net和 HybirdSN等方法相 比,本文算法能够有效提升总体精度、平均精度和 Kappa系数等客观评价指标。     

遥感信息多元综合分析及制图系统

运用多元综合分析的方法,对遥感制图的信息源、研究对象、地学分析作了重点研究。结合地学调查,从地理生物学原理与专题制图应用上进行了尝试。同时还概要地介绍了遥感专题机助制图地理参数辅助决策数据的应用。最后简明地阐述了遥感信息综合分析制图系统的构成及工艺流程。     

大气臭氧地基遥感反演的退卷积方法

本文提出了一种大气臭氧地基遥感信息处理的新方法,它利用非线性退卷积方法和等效仪器函数提高光谱分辨率,利用非线性最小二乘法进行大气臭氧反演计算,可以显着提高大气臭氧遥感的可探测高度和精度。