基于艇载遥感成像数据的高空间分辨率全色遥感图像仿真

高空间分辨率全色遥感图像在军事侦察、地面监视等领域具有较高的应用价值.为模拟星载全色遥感图像, 提出了一种由艇载遥感成像系统获取的低空遥感图像为数据源的高空间分辨率全色遥感图像仿真方法.首先将低空宽视场图像按典型地物类型进行监督分类, 其次将低空宽视场图像与多光谱图像按不同地物类型分类拟合, 并将多光谱拟合结果合成高空间分辨率全色仿真图像, 最后对高空间分辨率全色仿真图像进行仿真精度评价.相比星载全色遥感图像, 仿真图像同样具备高空间分辨率、全色波段、宽视场等特点.仿真方法可为星载全色遥感图像仿真提供较准确的数据支撑.     

Effect of spatial resolution on classification errors of pure andmixed pixels in remote sensing

It is observed in remote sensing that a finer spatial resolution does not necessarily improve the classification performance. These observations have been understood by using the conceptual explanation that "boundary effect" and "within-class variability" work against one another. Though easily understood, this conceptual explanation cannot be readily used for a quantitative investigation. The authors design a simulation scheme to evaluate systematically the impacts of various parameters on the classification accuracy. The authors employ a model for the class spectral covariance of pure pixels and a linear mixing model for the spectral responses of mixed pixels. Based on these models, the authors derive the statistical characteristics for mixed pixels and assess the corresponding classification errors. As the ratio of ground sampling distance to field size decreases, the classification error associated with pure pixels tends to increase, whereas the classification error associated with mixed pixels tends to decrease from the smaller area of mixed pixels. The simulation results show that the overall classification error first decreases with decreasing ratio of ground sampling distance to field width, reaches a minimum value, and then may increase with further decreasing ratio. The study on the classification error may help the development of classification schemes for high spatial resolution imagery     

Residential area extraction based on saliency analysis for high spatial resolution remote sensing images

Traditional residential area extraction methods for remote sensing image depend on classification, segmentation and prior knowledge which are time-consuming and difficult to build. In this paper, an efficient, saliency analysis-based residential area extraction method is proposed. In the proposed model, an adaptive directional prediction-based lifting wavelet transform (ADP-LWT) is introduced to obtain the orientation feature. A logarithm co-occurrence histogram is employed to compute the intensity feature. The color opponency and diagram objection based on the information are proposed to extract color feature from the contrast in the red–green opponent channel. The saliency map is obtained through a weighted combination based on the feature competition and the residential area is extracted by saliency map threshold segmentation. The experimental results reveal that the residential area extracted by our model has more demarcated boundaries and better performance in background subtraction.     

高分辨率遥感图像的聚类

高分辨率遥感图像中的细小目标(如道路等)使图像的同类区域表现变得不一致,从而增加了高分辨率图像聚类的难度,本文提出了一种高分辨率遥感图像的聚类方法,其聚类过程包括如下三个步骤:第一步,在滑动窗口内使用消除次要成分法处理遥感图像,该处理过程使用一维形态学分水岭技术获得直方图中的左侧阈值和右侧阈值,再根据这两个阈值滤除图像中的次要成分;第二步,计算滑动窗口内的图像特征;第三步根据图像特征量利用BPC(Back Propagation and Competitive)网络进行图像聚类。三组试验(本文提出的聚类算法,最邻近距离聚类法,K均值聚类法)表明本文提出的图像聚类方法可以有效实现高分辨率遥感图像的聚类。     

Simulation of optical remote sensing systems

Research on understanding the remote-sensing process as a system and investigating the interrelated effects of various parameter is described. A system model for the simulation of remote-sensing systems is presented. The system is divided into three parts: the scene, the sensor, and the processing algorithms. Models are presented and implemented for these component systems. Validation of the system model is considered over a specific test site. Results of the simulation for various scene and sensor configurations are included. Results of applying the model to various system configurations using simulated Landsat sensors are then presented to show how the simulation can be used to investigate the interrelated effects of system parameters.<>     

估算红外成像作用空间分辨率的新方法

提出了一种新的基于特征熵的估算红外成像作用空间分辨率的方法，研究红外目标在各分辨等级（发现、识别、确认）所需的红外成像空间分辨率。首先，通过下采样获取各分辨率等级下的目标图像；其次，提取目标的特征（纹理、形状和全局统计特征），并对目标的特征空间进行归一化，得到其特征直方图；然后通过计算其特征熵，度量目标的信息量；最后根据设定的阈值，给出红外目标在不同的识别等级下所需的最低成像分辨率结论，并以计算机仿真红外目标为例进行验证。实验结果表明:文中结论与识别结果较好吻合，能够为红外识别系统的优化设计和分析提供参考依据。     

星载高分辨率红外双谱段遥感器光学系统设计北大核心CSCD

针对高精度红外遥感成像的应用需求,提出了高空间分辨率、高噪声等效温差和大幅宽的中波/长波红外双谱段遥感成像技术方案,在太阳同步轨道获取地面目标的中波和长波红外辐射信息,谱段范围分别为3~5μm和8~12μm,中波红外谱段空间分辨率优于5 m,长波红外谱段空间分辨率优于10 m,幅宽大于20 km,噪声等效温差优于60 mK。根据噪声等效温差和光学传递函数等要求优化了光学系统指标参数,分析了二次成像同轴三反光学系统的冷光阑匹配原理,计算了光学系统初始结构参数,设计了光阑匹配型和出瞳匹配型中波/长波红外双谱段一体化光学系统,比较了两种方案光学系统的冷光阑匹配效果。     

高分辨率遥感图像融合技术的研究

图像融合已成为图像理解和计算机视觉领域中的一项重要而有用的新技术,多源遥感图像数据融合也成为遥感领域的研究热点,其目的是将来自多信息源的图像数据加以智能化合成,产生比单一传感器数据更精确、更可靠的描述和判决,使融合图像更符合人和机器的视觉特性,更有利于诸如目标检测与识别等进一步的图像理解与分析。     

基于EMD的红外遥测光谱信号预处理新方法

利用红外遥测光谱仪远距离快速、准确地探测污染气体并给出定性鉴别结果,必须对遥测光谱进行预处理,消除高频噪声和低频基线的干扰,提取出污染气体的特征光谱信号。针对现有方法的不足,提出采用具有自适应特性的EMD方法,对光谱信号进行无参数分解,提取出高频噪声与低频基线,实现了红外遥测光谱的预处理。经过该方法处理,光谱信号全局评估系数RMS1平均值达到0.141,局部评估系数RMS2平均值达到0.182,综合评估系数RMS*平均值达到0.026,明显优于小波方法。实验结果表明,EMD方法用于红外遥测光谱信号去噪与基线校正,算法简单,运行可靠,可使问题得到有效解决。     

高分遥感影像中道路信息提取方法综述

阐述了道路信息提取的重要性和一般步骤及基本思路,通过对高分辨率遥感图像中道路信息提取方法利用道路特征的不同进行了分类整理,并对经典方法进行分析,希望能对找到一种具有普适性的道路提取方法起到事半功倍的效果。在此基础上结合当前科技发展的方向,从道路提取的基本思想出发,对道路特征提取研究的前景做出展望。     

Integrated spectral and spatial information mining in remote sensing imagery

Most existing remote sensing image retrieval systems allow only simple queries based on sensor, location, and date of image capture. This approach does not permit the efficient retrieval of useful hidden information from large image databases. This paper presents an integrated approach to retrieving spectral and spatial patterns from remotely sensed imagery using state-of-the-art data mining and advanced database technologies. Land cover information corresponding to spectral characteristics is identified by supervised classification based on support vector machines with automatic model selection, while textural features characterizing spatial information are extracted using Gabor wavelet coefficients. Within identified land cover categories, textural features are clustered to acquire search-efficient space in an object-oriented database with associated images in an image database. Interesting patterns are then retrieved using a query-by-example approach. The evaluation of the study results using coverage and novelty measures validates the effectiveness of the proposed remote sensing image information mining framework, which is potentially useful for applications such as agricultural and environmental monitoring.     

大气遥感高光谱分辨率激光雷达研究进展

高光谱分辨率激光雷达由于可实现对大气参数的精确反演,在大气遥感领域具有较好的发展前景。介绍了高光谱分辨率激光雷达探测气溶胶、大气温度以及风速的基本原理以及目前国内外的研究进展,并重点介绍了高光谱分辨率激光雷达系统中的鉴频技术、激光技术、锁频技术以及数据处理技术等几项关键技术。     

空间查询技术在遥感数据编目处理中的应用

采用地理信息系统的空间查询技术,解决了遥感卫星数据编目系统对多元化地理信息的需求问题.描述了空间查询技术和编目系统,同时分析了二者相结合的优点.提出了将编目处理中生成的标准景图像视为几何点对象,经过投影转换在地理信息系统数据集中进行空间查询,获取丰富的地理信息以扩充编目元信息的方法.现的多元地理信息的编目系统丰富了遥感数据的描述信息,使得用户能够更直观、更方便、更有效地查询遥感数据,从而达到高效、便捷地利用遥感图像的目的.     

基于地标空间信息的高光谱遥感图像分类

高光谱遥感图像同时具有光谱与空间特征信息,充分利用空间特征能够有效提高分类结果.高光谱图像上同类像素点的邻域空间信息差异较大会影响分类效果,针对这个问题提出了一种新的空间信息利用方法,记作地标空间信息.将光谱信息与地标空间信息结合使用,通过深度信念网络提取特征.实验表明地标空间信息能够有效提高深度信念网络以及对比方法的分类结果.     

超敏捷动中成像高分辨率遥感卫星成像理论分析

对超敏捷动中成像遥感卫星角速度快（6（）/s）、角加速度大（1.5（）/s2）、成像参数随时空复杂多变等新问题,开展了超敏捷动中成像特点分析与成像参数仿真分析工作。构建了动中成像复杂模型,精确分析了动中成像合速度的变化规律。在此基础上,结合信噪比、调制传递函数（MTF）等计算公式,全面分析了不同成像条件下,动中成像系统的行频、TDI级数、姿态稳定度MTF、同步误差MTF、偏流修正误差MTF等随角速度的变化关系,为超敏捷动中成像卫星,尤其是卫星的成像电子学,提供了重要的设计依据。     

On the application of averaging median filters in remote sensing

The application of averaging median filters to remote sensing has been investigated, and the results are presented with some discussion and recommendations. Averaging median filters can be considered as a subclass of the standard median filters. For image processing purposes, a two-dimensional window is first filtered by a number of average filters, and the final result of the averaging filters is equal to the median of the central pixel value and the averaging filter results. Applications of this averaging median filter to Landsat images are presented, and the results show that the fine details are preserved while attenuating the impulsing noise     

多尺度多特征融合的高分辨率遥感影像分类

针对高分辨率遥感影像多尺度、空间分布复杂以及特征繁多的特点,从遥感影像特征提取的尺度效应以及各类地物显著性特征各异入手,提出了基于多尺度多特征融合的高分辨率遥感影像分类的方法。该方法构建最优尺度分割函数模型,寻找出各地物的最优尺度,分别提取影像的纹理、颜色和形状特征。在此基础上,利用各地物特征的显著性差异实现多尺度下多特征的加权融合。该加权融合方法突破了常规的最优尺度分割算法未能充分考虑各类地物特征差异性的局限性,通过分析各类地物的显著性,建立了各个特征在分类中所占权重的模型。实验结果表明：相对传统无监督分类算法,该方法准确率提高约7%,且运行效率高。     

Spectral band selection for visible-near infrared remote sensing:spectral-spatial resolution tradeoffs

Recent technological developments permit improved instrumentation for surveillance and resource monitoring, but tradeoffs of spectral resolution and number of spectral bands versus spatial resolution and measurement precision must be considered. A band selection procedure is applied to high spectral resolution (0.01 μ/m) aircraft sensor imagery representing the visible and near-infrared wavelengths (0.4-2.5 μm). Approximately 30-40 spectral bands characterize virtually all the information (variability) in the data, with the precise number depending on issues of data interpretation. This suggests that lower spectral resolution and higher spatial resolution are preferable to the reverse. Further study is needed to evaluate the significance of spectral bands having very low amplitude variability     

一种高分辨率遥感影像建筑物提取方法

采用了一种基于独特像元的快速自动提取建筑物的方法,如线与线之间的交叉点。首先,在预处理阶段采用边缘保留双边滤波器降低噪声,提高建筑物边缘的对比度;其次采用EDLines线段探测器实时准确地提取建筑物线段;最后,采用图表搜索感知分组的方法分层次组织预判线段为候选矩形建筑物。实验证明,该方法可以实现遥感影像建筑物的准确和快速提取。     

空间遥感用InGaAs探测器低噪声电路系统设计

InGaAs短波红外探测器对于空间遥感有着重要应用价值。为了实现空间遥感仪器提出的高信噪比要求,首先要降低InGaAs探测器后端信号获取电路自身的噪声水平,其次利用热电制冷器对InGaAs探测器制冷,并通过连续式温度控制电路保证热电制冷器在温控过程中对探测器无干扰,使探测器稳定地工作在合适的温度点,这样才能满足仪器的信噪比要求。结果表明,信号获取电路与温度控制电路两部分有效结合,整个低噪声电路系统能明显提高仪器信噪比。