陆地卫星图像在内陆湖地区铁路选线中的应用

集(集宁)——通(通辽)线横贯内蒙古自治区东、西部,线路全长900余公里,是计划了多年的一条长大铁路新干线。沿线地广人稀、交通困难、地面研究程度较低。1984年我院采用遥感方法,在该线东西长800公里,南北宽100公里的范围内利用陆地卫星图像、航空像片及1:5万地形图作了大面积、多方案的综合选线,并进行现场重点验证。1985年初,较好地完成了铁道部下达的可行性研究任务。本文仅以集通线大盐海子地区商都线路方案取舍为例,说明陆地卫星图像在内陆湖地区铁路选线中的应用效果。

应用Landsat-8卫星遥感图像的青海地区土壤分类研究

遥感技术的迅速发展和应用,为土壤分类提供了新的技术与数据支持,文章提出一种利用陆地卫星-8(Landsat-8)遥感图像的土壤分类方法,用于青海地区土壤分类,所提取的特征包括4种,即基于遥感图像的变换特征、指数特征、纹理特征和基于高程图像的地形特征,通过随机森林方法实现土壤分类。文章首先提出2个基于遥感图像的纹理特征--熵与平滑度,从中国土壤数据集中自动提取了16 444个数据点,并在试验中采用,充分验证了此分类方法的有效性。

计算机数字图像处理在提取隐伏地质构造信息中的应用

陆地卫星图像提供给人们的信息是十分丰富的,它除直接反映地表地物信息外,还包含大量的被第四系掩盖的隐伏地质构造信息。这些隐伏地质构造的信息是通过间接因素传输和反应的,由于信息量经过衰减,所反应的信息微弱,影像形迹隐晦,没有直接信息那样清楚和容易判别,容易被人们所忽视。但是这些信息是客观存在的,如何提取和研究这种有用的信息,是遥感地质学研究的新课题。

Using Landsat images to monitor changes in the snow-covered area of selected glaciers in northern Pakistan

Landsat satellite images were used to map and monitor the snow-covered areas of four glaciers with different aspects(Passu: 36.473°N, 74.766°E;Momhil: 36.394°N, 75.085°E; Trivor: 36.249°N,74.968°E; and Kunyang: 36.083°N, 75.288°E) in the upper Indus basin, northern Pakistan, from 1990-2014. The snow-covered areas of the selected glaciers were identified and classified using supervised and rule-based image analysis techniques in three different seasons. Accuracy assessment of the classified images indicated that the supervised classification technique performed slightly better than the rule-based technique. Snow-covered areas on the selected glaciers were generally reduced during the study period but at different rates. Glaciers reached maximum areal snow coverage in winter and premonsoon seasons and minimum areal snow coverage in monsoon seasons, with the lowest snow-covered area occurring in August and September. The snowcovered area on Passu glacier decreased by 24.50%,3.15% and 11.25% in the pre-monsoon, monsoon and post-monsoon seasons, respectively. Similarly, the other three glaciers showed notable decreases in snow-covered area during the pre-and post-monsoon seasons; however, no clear changes were observed during monsoon seasons. During pre-monsoon seasons, the eastward-facing glacier lost comparatively more snow-covered area than the westward-facing glacier. The average seasonal glacier surface temperature calculated from the Landsat thermal band showed negative correlations of-0.67,-0.89,-0.75 and-0.77 with the average seasonal snowcovered areas of the Passu, Momhil, Trivor and Kunyang glaciers, respectively, during pre-monsoon seasons. Similarly, the air temperature collected from a nearby meteorological station showed an increasing trend, indicating that the snow-covered area reduction in the region was largely due to climate warming.

Monitoring of coastline changes along the Red Sea,Yemen based on remote sensing technique

The coastline changes along Yemen＇s the Red Sea （Al-muka, Al-khohah, Al-tiaf, Ras Katib and Al- Urji spits） were studied using a series of landsat images （MSS, TM and ETM ＋ , 1972, 1989, 2000, 2006）, coupled with geomorphological, sedimentological and meteorological findings. Comparison of satellite images provided a viable means for establishing long-term coastal changes （accretion and erosion） as observed in the studied spits （Al-Urj, Ras Sham, Ras Maemoon, Ras Katib, Al-Mandar, Nukhaylah, Mujamilah, Ras Ashab Abu-Zahr and Mukha）. The rate of the spit accretion has the greatest value up to 89 pixel/year corresponding to 72 290 m2/year in Mukha, while the spit erosion shows greatest value up to 131 pixel/year corresponding to 106 404 m2/year in Mujamilab. The patterns of accretion and erosion along the spits depend on the spit direction, natural processes mainly of wave-induced longshore currents, the sediments supply and depth of sea.

Land Cover Classification with Multi-source Data Using Evidential Reasoning Approach

Land cover classification is the core of converting satellite imagery to available geographic data.However,spectral signatures do not always provide enough information in classification decisions.Thus,the application of multi-source data becomes necessary.This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery,altitude and slope data.Results show that multi-source data contribute to the classification accuracy achieved by the ER method,whereas play a negative role to that derived by maximum likelihood classifier (MLC).In comparison to the results derived based on TM imagery alone,the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible.The ER method is regarded as a better approach for multi-source image classification.In addition,the method produces not only an accurate classification result,but also the uncertainty which presents the inherent difficulty in classification decisions.The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.

Robust Classification of Remote Sensing Data for Green Space Analysis

All of the Landsat 7 data collected after 2003 contains missing pixels in the form of unsightly stripes across the images. To recover missing data of a Landsat image, different methods may be used. However, the gap filling process creates inconsistencies on pixel intensity values. The incongruous pixel numbers are anomolous observations and their classification in the reference specter is challenging. In an effort to contribute to this need, we propose a reliable robust approach to classify inconsistent pixels after the gap filling process. To estimate multivariate location-scale parameters a new robust DMVV （depth minimum vector variance estimator） is presented. The DMVV algorithm does not require any matrix inversion for its calculation, consequently its computational time is highly reduced. The results show that it has a high breakdown point and is very efficient for large data set. Landsat remote sensing data of Jakarta Province across years 2002 and 2010 are used as case study.

联合云量自动评估和加权支持向量机的Landsat图像云检测

针对云量自动评估算法难以检测Landsat图像中的半透明云问题,提出一种云量自动评估和加权支持向量机相结合的云检测算法。首先根据云在不同波段中的大气辐射特点,结合陆地卫星ETM+图像数据的光谱特性,利用云量自动评估算法将图像像元初步分成云像元、非云像元和待定像元,再以云的光谱特性构造特征向量,利用加权支持向量机算法进行待定像元的云层检测,最终获得全部图像的云检测结果。仿真试验结果表明,该方法既具有云量自动评估算法的云检测优势,还对云量自动评估算法难以识别的半透明云有较好的检测效果。

An improved Landsat Image Mosaic of Antarctica

A revised Landsat Image Mosaic of Antarctica （LIMA） is presented, using the 1073 multi-band scenes of the original Land- sat-7 ETM＋ LIMA image collection available at the United States Geological Survey （USGS： http：//lima.usgs.gov/）. Three improvements have been applied during the data processing： （1） DN saturation is adjusted by adopting a linear regression, which has a lower root mean square error than the ratio regression used by LIMA; （2） solar elevation angle is calculated using pixel-level latitude/longitude and the acquisition time and date of the central pixel of the scene, improving slightly upon the bi- linear interpolation of the solar elevation angles of scene comers applied in LIMA; and （3） two additional image bands, Band 5 and Band 7, are sharpened using the panchromatic band （Band 8） and a Gram-Schmidt Spectral Sharpening algorithm to more easily distinguish snow, cloud and exposed rocks. The final planetary reflectance product is stored in 16-bit bands to preserve the full radiometric content of the scenes. A comparative statistical analysis among 12 sample regions indicates that the new mosaic has enhanced visual qualities, information entropy, and information content for land cover classification relative to LIMA.