中巴资源一号卫星南京幅数据质量与几何纠正评价

首先对中巴资源一号卫星主要技术参数与性能指标作了介绍,然后选取了高分辨率CCD相 机接收的2、3、4波段数据(南京地区)进行处理,发现2波段中有若干明显的条带,找出了引起条带 的原因,通过未去除条带3个波段的合成图像与去除条带后3个波段的合成图像相比,图像质量有 明显的改善,分别对两种合成图像进行几何纠正,发现后者效果明显好于前者。对几何纠正后的图 像进行了分析,地面分辨率达20 m,可用于土地利用、森林覆盖、地质和农业等专题信息的提取。     

北京一号小卫星几何纠正方法与试验

针对北京一号卫星传感器成像技术特点,在相应的共线方程成像模型基础上,设计了详细的几何纠正流程.该流程包含外方位元素求解和影像重采样两个步骤.在外方位元素求解方面,提出了一种求解外方位元素初始值的计算方法;在影像重采样方面,本文提出了一种求解影像行的迭代方法.最后用本文提出的几何纠正流程对一幅北京一号小卫星影像进行纠正实验,并与传统的多项式纠正进行了精度比较分析,得出本文方法比多项式纠正精度高而且可以消除由于高程引起的投影差.     

断口图像中疲劳条带的自动分割与测量

金属断口图像中疲劳条带区域的分割和间距的测量是断口定量分析疲劳寿命和疲劳应力的重要部分.提出条带区域的粗细两级自动分割方法,并建立条带间距测量的自相关函数法模型:首先,利用灰度共生矩阵的熵对断口进行粗分割,得到条带类区域;用平方差累积法确定条带的方向,再分别在条带法向和切向上对图像进行细分割,确定出条带矩形区域;然后,采用自相关函数法计算条带间距.实际的断口图像样本测试表明本文的方法在疲劳条带的自动分割与测量中是有效的.     

基于小波变换的多波段遥感图像条带噪声的去除

介绍了几种在TM，MSS，SPOT等多传感器遥感图像中条带噪声去除方法的特点，提出了一种基于小波变换的条带噪声去除方法，并以几何纠正前的非均匀地物分布的CMODIS图像为实验数据，对这些方法的去条带噪声效果作了比较。结果表明，本提出的方法要优于以前的几种常用方法，具有很好的去条带效果，同时较好地保持了原图像的特征。这种方法在其它多传感器遥感图像的条带噪声去除中也有很强的适用性。     

一种改进的矩匹配方法在CMODIS数据条带去除中的应用

由于传感器之间对接受的地物辐射信号的响应特性不同,导致CMODIS数据中的许多波段含有大量的条带。这些噪声严重影响了CMODIS数据的解译和信息提取。介绍了几种常用在TM、MSS、SPOT等多传感器光谱仪中条带去除方法,提出了一种改进矩匹配方法用于CMODIS数据中的条带去除,并比较了这种方法和其它几种常用方法对几何纠正前非均匀地物分布的CMODIS数据的去条带噪声结果。结果表明这种新方法要优于以上提到的几种常用方法,具有很好的去条带噪声效果,同时保持图像原有的的信息。这种方法在其它多传感器遥感图像的条带噪声去除中也有很强的适用性。     

线性方程的两个算法

本文介绍线性方程的列初等变换求解算法和一种新的行初等变换求解算法。这两个算法的共同特点是，都使线性不定方程和线性确定方法的求解过程同一化，都可用于对任意线性方程或方程组的求解。尤其是列初等变换求解算法的研究成功，用事实纠正了在数学界长期认为的“只用列初等变换或用行、列初等变换不能解线性方法组”［１］的错误结论。     

机载SAR图像的灰度校正

由于我国机载合成孔径雷达(SAR)系统存在的一些缺陷,雷达图像有时会出现方位向的非正常条带。本文研究了一种新的利用计算机进行数字处理去除这种条带的方法——行统计量增强法,并介绍了用这种方法进行试验所得到的结果。     

扫描数字化地形图纠正拼接系统的高效系统设计

地形图扫描数字化的前期工作需要解决两个重点问题, 即纠正和拼接。传统方法是利用图像处理软件逐个选取并输入控制点, 然后进行纠正。该方法是一种具有普遍适用性的图像纠正方法,但需要一个个地输入控制点坐标, 操作起来繁琐费时。在实验室环境下, 没有大型扫描仪, 纠正完成的地形图尚需要进行拼接。在分析扫描地形图变形特点的基础上, 提出了变形进行纠正的经验公式, 并用VC+ + 开发了一个针对地形图的纠正和拼接的专用软件。其最大的优点是图块纠正时不需要逐点输入控制点坐标, 拼接时方便快捷。最后分别用了绝对检验方法和相对检验方法对公里网格点和网格点内部坐标进行检验, 表明其误差不超过0. 3%。     

一种分离低维信号的ICA快速算法

介绍了一种基于低维反对称矩阵指数的快速独立分量分析算法. 由于算法中牵涉到的矩阵指数具有解析闭合形式的表达, 因而算法中使用到的矩阵指数以及最优下降方向均可解析地得到. 另外, 我们纠正了在别的文献中建立的四维反对称矩阵指数表达式中的两个错误. 最后, 我们用仿真验证了算法. 实验结果表明: 相比于广为应用的Extended InfoMax和FastICA算法, 本文算法能得到更佳的分离性能.     

CBERS-1影像全国数字镶嵌与制图

中巴资源一号卫星在轨正常运行4年多积累了丰富影像数据，它为研制我国全国数字影像图提供了坚实的基础。本文介绍了CBERS-1噪声条带去除、图像增强、几何纠正和镶嵌、色调调整等方面的关键技术，并全面、系统地总结了基于CBERS-1数据制作全国数字镶嵌图的作业流程及制图规范。     

Integration of Landsat and aeromagnetic data as aid to the structural analysis of Crete and S.E. Peloponessus

Abstract

The paper comments on the usefulness of remotely-sensed data (Land-sat MSS images in both digital and photographic format—aeromagnetic data) in the tectonic analysis of areas of Greece. The island of Crete was the main case study area, while a general analysis was also carried out in the South Eastern Peloponessus.

Both image processing (spectral and spatial analysis) of the Landsat CCTs of Crete and computer analysis of the features mapped on the images have been carried out. Aeromagnetic data are also analysed using advanced processing techniques (spectral analysis and deconvolution). Structural interpretations are improved by the study of the enhanced Landsat images and aeromagnetic maps, while the use of the various computer techniques makes the analysis of the mapped patterns easier and more accurate.

The combined interpretation of aeromagnetic and Landsat MSS data added several significant structural features, previously unrecognised from separate interpretations of aeromagnetic data and Landsat images.     

Population estimation from Landsat imagery

The paper introduces a method of population estimation using the Landsat MSS data. The radiance in the four spectral bands, detected by the multi spectral scanner (MSS) depends upon the ground covering materials, albeit the land use of the area. A mathematical model is set up to express the relation between the reflected electromagnetic energy of sample areas and their population distribution. Landsat 1 and Landsat 3 data of the Kanto area (including Tokyo Metropolitan), acquired in 1972 and 1979, are used along with ground-based census data of 1970 and 1975 to monitor the population distribution and its temporal changes. The method provided a reliable assessment of the population density in residential zones, however land-use classification using MSS imagery previous to the modeling is expected to improve the results.     

The utility of multi-sensor data for mapping eroded lands

The availability of remote sensing data with improved spatial, spectral and radiometric resolution is now available to fully exploit their potential for a specific application subject to the relative merits and the limitations of each sensor's data. Presented here is a case study where Landsat MSS and TM; and SPOT MLA data for part of the Bijapur district, southern India, which were acquired on the same day, have been evaluated for mapping eroded lands. The approach involves the geometric registration of all three data to a common map grid using tie points and third order polynomial transform; and resampling the MSS and TM data to a 20m by 20 m pixel dimension and radiometric normalization. Thematic maps showing eroded lands were generated on a micro-VAXbased DIPIX system using a maximum likelihood classifier. Accuracy estimates were made for the thematic maps following stratified unaligned random sampling technique, and subsequently, computing overall accuracy and Kappa coefficient. Spectral separability and classification accuracy was maximum from SPOT-MLA data followed by a combination of Landsat MSS band 1, SPOT-MLA band 2 and Landsat TM band 4; Landsat TM, a combination of Landsat MSS, TM and SPOT MLA; and Landsat MSS data.     

卫星遥感影像预处理中噪声去除方法的研究

主要介绍了在遥感卫星影像预处理过程中遇到的噪声问题及其解决方法,内容有常见的遥感噪声现象包括整像素偏移、字节错位、影像错行、带状噪声、条纹噪声等,在描述噪声现象的同时给出了针对上述噪声的相应处理方法如空域滤波方法以及频域滤波方法等。实践证明所述方法在遥感卫星影像预处理过程中得到了很好地应用。     

Regression and ratio estimators to integrate AVHRR and MSS data

Regression and ratio estimators are used to integrate AVHRR-GAC and Landsat MSS digital data to estimate forest area in the continental United States. Forestlands are enumerated for the 48 contiguous states using five different AVHRR-GAC data sets. The five GAC data sets tested, each with a spatial resolution of 4 km, were composed of different combinations of vegetation index and thermal data acquired over the nine month growing period in 1984. Twenty Landsat MSS scenes were selected countrywide and used to calibrate AVHRR forest estimates. Results indicated that the GAC and MSS forest estimates were not highly correlated; R² values ranged from 0.5 to 0.7. Although the ratio of means and linear regression corrections were, on the average, closer to national U.S. Forest Service forest area estimates, these correction procedures did not consistently improve GAC estimates of forest area. GAC forest area estimates tended to be high in densely forested regions such as the northeast and low in sparsely forested areas. This fact, and the low correlation coefficients, indicate that AVHRR data should be used for primary stratification (with MSS as the second stage) and not as an auxiliary variable in a regression correction procedure.     

Multi-temporal and multi-source cartography of the glacial cover of Nevado Coropuna (Arequipa,Peru) between 1955 and 2003

This study reports on the glacial cover evolution of the Nevado Coropuna between 1955 and 2003, based on Peruvian topographic maps and satellite images taken from the Landsat 2 and 5 multispectral scanner (MSS), Landsat 5 Thematic Mapper (TM) and Landsat 7 (ETM+). The normalized difference snow index has been applied to these images to estimate the glacierized area of Coropuna. The satellite-based results show that the glacier area was 105 ± 16 km² in 1975 (Landsat 2 MSS), which then reduced to 96 ± 15 km² in 1985 (Landsat 5 MSS), 64 ± 8 km² in 1996 (Landsat 5 TM) and 56 ± 6 km² in 2003 (Landsat 5 TM). Altogether, between 1955 and 2003, Coropuna lost 66 km² of its glacial cover, which represents a mean retreat of 1.4 km² year^?1, that is, a loss of 54% in 48 years (11% loss per decade). The maximum rate of retreat occurred during the 1980s and 1990s, a phenomenon probably linked with the pluviometric deficit of El Niño events of 1983 and 1992.     

Relationship between Landsat MSS data and forest tree parameters

Abstract

Band ratios, indices and radiance in the four channels of the Multi-spectral Scanner (MSS) on the Landsat-4 satellite for October 1984 and March 1985 were correlated with mean tree parameters of teak plantations (age, mean tree height, mean tree diameter at breast height, mean canopy diameter, mean canopy volume). The Landsat MSS data for March (when teak trees are leafless) were more suitable than the Landsat MSS data for October for categorizing tree parameters.     

Effect of Landsat thematic mapper sensor parameters on land cover classification

Selected sensor parameter differences between TM and MSS were assessed through classification performance of a suburban/regional test site. Overall classification accuracy of a seven-band Landsat TM scene in comparison to MSS yielded an improvement in accuracy from 74.8% to 83.2%. To study the possible causes for the difference in classification performance, key sensor parameter differences between MSS and TM, including 1) spatial resolution (30 m for TM versus 80 m for MSS), 2) quantization level (256 levels for TM versus 64 for MSS), and 3) spectral regions (seven bands in four major spectral regions for TM versus four bands in two regions for MSS), were evaluated. Landsat TM data were processed to simulate all possible combinations of these MSS and TM parameters, yielding a three-factor design with two levels per factor. The results indicated that the added spectral regions (TM 1, TM 5, and TM 7) and to a lesser degree the increase in quantization level to eight bits produced the improved TM classification accuracy. However, in this study, the higher 30 m spatial resolution of TM contributed to a reduced classification accuracy from increased within-field variability or class heterogeneity.     

Application of thematic mapper data to corn and soybean development stage estimation

A model, utilizing direct relationship between remotely sensed spectral data and the development stage of both corn and soybeans has been proposed and published previously (Badhwar and Henderson, 1981; and Henderson and Badhwar, 1984). This model was developed using data acquired by instruments mounted on trucks over field plots of corn and soybeans as well as satellite data from Landsat. In all cases, the data was analyzed in the spectral bands equivalent to the four bands of Landsat multispectral scanner (MSS). In this study the same model has been applied to corn and soybeans using Landsat-4 Thematic Mapper (TM) data combined with simulated TM data to provide a multitemporal data set in TM band intervals. All data (five total acquisitions) were acquired over a test site in Webster County, Iowa from June to October 1982. The use of TM data for determining development state is as accurate as with Landsat MSS and field plot data in MSS bands. The maximum deviation of 0.6 development stage for corn and 0.8 development stage for soybeans is well within the uncertainty with which a field can be estimated with procedures used by observers on the ground in 1982.     

Extension of retrospective datasets using multiple sensors. An approach to radiometric intercalibration of Landsat TM and MSS data

The establishment of monitoring frameworks for environmental problems is frequently based on retrospective, multi-temporal series of satellite images. The derivation of concise conclusions from these series largely depends on their quantitative consistency; hence, a completely standardized analysis of multi-temporal time series is mandatory. In the context of the required radiometric rectification, sensor calibration is an essential component, the accuracy of which largely determines the overall result. While for newer sensor systems extensive documentation on the development of the sensor's sensitivity exists, older systems often lack the corresponding information.A methodology has been developed, tested and validated to radiometrically intercalibrate different sensor systems based on the precondition of simultaneous image acquisition, which is for example provided by the Landsat TM and MSS sensor systems hosted on Landsat 5. Different interpolation and processing steps within the procedure have been analyzed, and an intercalibration scheme is proposed to derive Landsat MSS calibration factors from Landsat TM, which is used as a calibrated reference. It supports a full radiometric rectification of Landsat MSS data and enables their incorporation into satellite image time series, which can thus be significantly extended by a maximum of 12 years.