可重构比特置换网络配置信息提取算法研究*

比特置换单元由比特置换网络和配置信息组成,基于Benes网络实现可重构比特置换网络,并改进和实现了两种配置信息提取算法,即二分法和并行算法。这两种方法能有效控制Benes网络中各开关元件的状态,实现各个待置换的比特在网络中非阻塞正确选路,其各有特点,在应用中可根据实际需要加以选择。     

大倾角光学遥感中大气点扩散函数的近似模型

大气对垂直遥感和大倾角遥感影响的主要差别之一体现于邻近象元的影响，对于垂直遥感，点扩散函数是各向同性的，即邻近象元对于目标象元的影响只与它们之间的距离有关，和它们之间的相对方位无关，但是对于大倾角遥感，点扩散函数不仅依赖于邻近象元和目标象元之间的距离，而且还依赖于它们之间的相对方位，在距离相同时，在观测方位上的邻近象元对目标象元的影响最大。本文用传感器，目标象元和邻近元构成的几何关系描述来自邻近象元的漫反射再经大气的一次散射而达到传感器的辐射通量，从而得到大气的点扩散函数的近似解析解，这种解析近似同蒙托卡洛的模拟结果有很好的一致，我们用这种方法得到了ASAS（Advanced Solid-state Array Spectroradiameter)大倾角观测时的大气的点扩散函数，并设计了一个Wiener滤波器去除邻近象元的影响，视觉效果评价和空间相关性分析的结果表明了这种方法的有效性和可行性。     

线阵CCD测量干涉条纹抽样问题的讨论

对利用线阵CCD测量干涉条件中，由于CCD本身的分离性和象元的积分效应，以及干涉条纹（余弦信号）抽样的特殊性，深入讨论了“如何确定可测量条纹最大空间频率”、“象元积分效应对抽样的影响”和“象元相位匹配对条纹对比度的影响”等3个问题，结果对于利用线阵CCD测量干涉条纹具有一般性的指导意义。     

图象局部域缺损信息恢复

图象某些象元受损称信息缺损。为了修复多点连通信息缺损,提出了纹理拟合方案。基于局部域上象元的整体相关性和基图象纹理连续性,对已知象元进行迭代拟合,同时修复多点缺损数据。实验结果表明修复区具有边界光滑和纹理连续的特点。     

基于混合像元分解和双边界提取的湖泊面积变化监测

如何通过充分考虑地物复杂性和地域时间限制,提高混合像元分解算法的精度和普适性,是目前基于中低分辨率遥感数据进行湖泊面积监测所面临的主要问题。针对问题,提出了一种结合双边界提取和混合象元分解的高效算法,最后基于AVHRR数据对中国东北,内蒙古地区的湖泊面积变化进行遥感监测,验证了算法的高精度和可行性。     

一种求解混合零空闲置换流水车间调度禁忌分布估计算法

结合混合零空闲置换流水车间调度问题MNPFSP(Mixed no-idle permutation flowshop scheduling problem)的特性,运用基于概率模型的分布估计算法解决该问题。算法将启发式算法融入分布估计算法中提高了初始解的质量。为了避免算法陷入局部最优,将禁忌算法融入分布估计算法中,提出一种禁忌分布估计算法求解混合零空闲置换流水车间问题。为了提高种群的多样性,加入了三种邻域搜索。实例测试结果显示,该算法求解混合零空闲置换流水车间问题具有很好的优势。     

空间信息在图象分类中的应用

空间信息指纹理、局部结构、形状信息.空间信息反映图象的几何特征,表达诸相邻象元 在空间上的关系.本文应用纹理、局部结构和松弛法对林区图象分类,并与基于单个象元的光 谱信息分类结果做了比较.     

基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究北大核心CSCD

近年来湿地的植被退化一直是全球关注的问题,对湿地植被覆盖度进行反演并研究其时空分布特征显得尤为重要。而为了解决植被反演中存在的混合像元问题,提出了基于面向对象的多端元光谱混合分析方法。以扎龙湿地保护区为研究对象,中高分辨率Landsat影像为数据源,从时间尺度和植被覆盖度等级变化层面,研究湿地植被时空变化特征。面向对象多端元混解模型,有效地减少了计算量和混合像元的端元变化,且反演值与检验值相关性较高,均方根误差较小,优于传统多端元混解模型方法,提高了植被覆盖度反演精度。扎龙湿地多年植被覆盖度整体呈现退化趋势,2001～2017年的平均变化速率高于1985～2000年,对于提高全球气候变化情景下植被转移预测精度具有重要理论意义。     

基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究

近年来湿地的植被退化一直是全球关注的问题,对湿地植被覆盖度进行反演并研究其时空分布特征显得尤为重要。而为了解决植被反演中存在的混合像元问题,提出了基于面向对象的多端元光谱混合分析方法。以扎龙湿地保护区为研究对象,中高分辨率Landsat影像为数据源,从时间尺度和植被覆盖度等级变化层面,研究湿地植被时空变化特征。面向对象多端元混解模型,有效地减少了计算量和混合像元的端元变化,且反演值与检验值相关性较高,均方根误差较小,优于传统多端元混解模型方法,提高了植被覆盖度反演精度。扎龙湿地多年植被覆盖度整体呈现退化趋势,2001～2017年的平均变化速率高于1985～2000年,对于提高全球气候变化情景下植被转移预测精度具有重要理论意义。     

USLE/RUSLE模型中植被覆盖因子多光谱影像计算

在各个土壤侵蚀模型中,准确确定植被覆盖影响因子是一项重要的工作。在以往的研究和应用过程中,对植被盖度值的确定通常是通过对地表植被覆盖类型或借助植被指数进行分级赋值,这些方法存在着分类标准的不确定性和较大随机误差。本文针对水土流失方程中植被盖度确定问题,利用多光谱数据,使用改进型植被指数分析及混合像元线性分解方法,结合地面调查数据,对土壤侵蚀模型中植被因子进行了遥感定量分析,实验结果证明了这种方法的优势所在。     

A geometric approach to a mixed pixel problem: Detecting subpixel woody vegetation

In digital satellite imagery, small fragments of woody vegetation are difficult to detect because they frequently are smaller than the pixel size and are mixed with other land cover classes. A method for detecting subpixel woody vegetation, which analyzes mixture phenomena at the individual pixel level, is presented. This method relies on a moving window to collect training sets for adjacent land cover. In order to locate pixels of interest and to decrease noise, image-derived masks are integrated with the original digital imagery in a geocoded information system. A rule-based scheme is employed to organize relative spatial and spectral information into classification decision procedures. Tests using simulated multispectral and panchromatic SPOT HRV imagery of lowland Britain have shown that the developed method discriminates significantly more woody vegetation than standard multispectral classification.     

Unmixing of soil types and estimation of soil exposure with simulated hyperspectral imagery

Hyperspectral determination of soil types has the potential to become an important addition to the methods used for classification and mapping of soils. In this study laboratory measured spectra of different soils, vegetation and crop residue were combined to simulate hyperspectral remote sensing imagery. The overall aim was to examine the spectral unmixing of these materials under laboratory conditions to better understand the limits to prediction of soil types and determination of cover fractions. Two different methods were utilized to mix spectra of the soil and vegetation and substantial differences were observed in the unmixing results from the different image types, particularly in mixed pixels. Results found pure soils were easily distinguished from each other when not mixed with vegetation, while some mixes of soil and vegetation were confused as pure soil spectra. The accuracy of abundance fractions retrieved in the unmixing process also varied substantially with soil type and vegetation cover.     

提高森林覆盖区积雪提取精度的方法研究——以玛纳斯河流域为例

森林覆盖区积雪的提取精度很低,由于植被冠层的遮挡,冠层下的积雪很难被提取出来。基于Landsat 8OLI数据,针对玛纳斯河流域下游有大面积森林覆盖的特点,通过传统的积雪指数法,结合NDVI数据的积雪指数法和面向对象图像特征法分别提取积雪面积。结果表明:1传统的NDSI和S3积雪指数法无法较好地提取出森林覆盖下的积雪,提取精度分别为85.23%和87.54%。这两种方法适用于空间尺度较大、植被覆盖面积较大的区域,并不适合所选研究区;2结合NDVI数据后的NDSI、S3积雪指数模型能大大提高森林覆盖下的积雪面积,提取精度分别达到91.47%和90.60%。在影像空间分辨率较高,流域尺度较小,林区覆盖较多的情况下可采用此方法提取积雪;3随着海拔的升高,地形阴影影响逐渐增大,NDVI辅助积雪指数方法提取林区覆盖下积雪面积逐渐减小。因此采用光谱、纹理和空间信息结合的面向对象图像特征方法提取积雪,能够较好地识别出受地形影响下的雪像元,精度达到89.75%,可以满足实际应用的需求。     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

Non-linear mixture modelling without end-members using an artificial neural network

Many methods of analysing remotely sensed data assume that pixels are pure, and so a failure to accommodate mixed pixels may result in significant errors in data interpretation and analysis. The analysis of data containing a large proportion of mixed pixels may therefore benefit from the decomposition of the pixels into their component parts. Methods for unmixing the composition of pixels have been used in a range of studies and have often increased the accuracy of the analyses. However, many of the methods assume linear mixing and require end-member spectra, but mixing is often non-linear and end-member spectra are difficult to obtain. In this paper, an alternative approach to unmixing the composition of image pixels, which makes no assumptions about the nature of the mixing and does not require end-member spectra, is presented. The method is based on an artificial neural network (ANN) and shown in a case study to provide accurate estimates of sub-pixel land cover composition. The results of this case study showed that accurate estimates of the proportional cover of a class and its areal extent may be made. It was also shown that there was a tendency for the accuracy of the unmixing to increase with the complexity of the network and the intensity of training. The results indicate the potential to derive accurate information from remotely sensed data sets dominated by mixed pixels.     

基于光谱和时相特征的夏玉米遥感识别

精确提取作物种植面积一直是农业遥感关注的主要问题之一。综合运用低分辨率的时相变化特征和中分辨率的光谱特征,提出一种夏玉米识别方法。首先基于MODIS NDVI时间序列曲线,分析夏玉米在时相变化上的识别特征,构建识别模型。夏玉米纯像元利用识别模型识别,而耕地和非耕地类型的植被产生的混合像元,则基于像元分解办法获取耕地组分的NDVI时序特征,再利用识别模型判定,然后结合土地利用数据根据空间关系得到中分辨率结果;玉米与其他作物的混合像元则利用中分辨率尺度光谱差异加以区分。研究结果表明,在伊洛河流域主要农业区,识别精度达到90.33%,为作物类型识别提供了新的思路。     

Smoke plume detection in the eastern United States using MODIS

In the eastern United States large amounts of smoke emitted from both wildfires and prescribed fires affect the regional air quality and long‐term climate and may have an impact on public health. Satellite remote sensing is an effective approach for detecting and monitoring the smoke plume. The spectral characteristics of smoke plume are measurably different from those of other cover types, such as vegetation, cloud, snow, and so on. A multi‐threshold method has been developed for detecting smoke plumes with eight MODIS spectral bands based on the analysis of spectral characteristics of different cover types. A series of tests are applied to all pixels in one granule (5‐min measurements) to filter out non‐smoke pixels step by step with water masking. At each step, specific thresholds are utilized. The results have been validated with true color images for a number of cases from different areas and time, showing that the algorithm works well except for a few missing or incorrect identified smoke pixels.     

Using genetic algorithms in sub-pixel mapping

In remotely sensed images, mixed pixels will always be present. Soft classification defines the membership degree of these pixels for the different land cover classes. Sub-pixel mapping is a technique designed to use the information contained in these mixed pixels to obtain a sharpened image. Pixels are divided into sub-pixels, representing the land cover class fractions. Genetic algorithms combined with the assumption of spatial dependence assign a location to every sub-pixel. The algorithm was tested on synthetic and degraded real imagery. Obtained accuracy measures were higher compared with conventional hard classifications.     

一种简单的估算植被覆盖度和恢复背景信息的方法

植被覆盖度是评估生态环境的一个重要参数，其对于全球环境变化和监测研究具有重要意义．如何从遥感资料估算植被覆盖度，并提高估算精度是建立全球或区域气候、生态模型的基础工作．该文从分析土壤、植被光谱信号的特点出发，根据植被覆盖度的定义，推导出计算植被覆盖度的方法，并进一步提出了计算植被覆盖度的三波段最大梯度差法．在此基础上，对部分植被覆盖下的土壤光谱实现重建．上述方法实现简单，适用范围广，并可有效分离植被、土壤的影响，因而有望替代常用的通过NDVI估算植被覆盖度的方法．