期刊：
Proceedings of SPIE - The International Society for Optical Engineering,2009年7471 ISSN：0277-786X
通讯作者：
Zhang, H.(zhang@caf.ac.cn)
作者机构：
[Zhang, Huaiqing; Ling, Chengxing] Institute of Forestry Resource Information and Techniques, Chinese Academy of Forestry, Beijing, 100091, China;[Lin, Hui; Ling, Chengxing] Research Center of Forest Remote Sensing and Information Engineering, CSUFT, Changsha, 410004, China
会议名称：
Second International Conference on Earth Observation for Global Changes
会议时间：
Chengdu, China
会议论文集名称：
Second International Conference on Earth Observation for Global Changes
摘要：
Image segmentation is a process of delineating an image into homogeneous polygons related to objects on the ground, and it is the foundation for further image analysis and interpretation. Low- or medium-resolution remotely sensed image usually leads to low accuracy of image segmentation because of large pixel sizes and a lot of mixed pixels. Thus, high-resolution image will probably result in increase of image segmentation accuracy because of smaller area covered by each pixel and reduced mixed pixels. This paper presents a study of QuickBird image segmentation for classification of land covers by mean-shift algorithm, the study area includes 1024 * 1024 pixels. The result showed that: the mean-shift algorithm led to a high accuracy of classification and Computing time for segmentation at different scales was also analyzed.
摘要：
The correlation analysis between biochemical contents of trees and in situ hyperspectral measurements provides the basis for forest remote sensing. Because trees especially arbor trees often are very tall and have a long growth period, it is very hard to collect hyperspectral measurements of crowns and thus it is difficult to analyze the relationships of their biochemical contents with hyperspectral data. This study focuses on the correlation analysis between the biochemical contents of young camphor trees and hyperspectral measurements obtained using a hyperspectrum analyzer from the Analytical Spectral Devices (ASD). The results show that chlorophyll-a, chlorophyll-b, and chlorophyll-ab are significantly correlated with the maximum of the first differential of red-edge and the reflectance of low-point of red-edge, while the correlation of carotene cellulose, phosphorus, and nitrogen with the hyperspectral location variables is very weak. Moreover, chlorophyll-a, chlorophyll-b, chlorophyll-ab, and phosphorus have significant correlation with the area of blue-edge but not with the areas of yellow-edge and red-edge. Carotene, cellulose, and nitrogen are weakly correlated with all the hyperspectral area variables. Chlorophyll-a, chlorophyll-b, and chlorophyll-ab have statistically significant correlation with the vegetation indices including Rg/Ro, (Rg-Ro)/ (Rg+Ro), SDr/SDb, (SDr-SDb)/ (SDr+SDb), and weak correlation with SDr/SDy and (SDr-SDb)/ (SDr+SDb). Phosphorus is significantly correlated with SDr/SDb, (SDr-SDb)/ (SDr+SDb) and weakly correlated with other vegetation indices. In addition, the correlation of carotene, cellulose, and nitrogen with the vegetation indices is not significant. These findings provide the basis on which the models that account for the relationships of biochemical contents with hyperspectral variables can be developed.
摘要：
The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection in forest regions. Firstly, multidate remote sensing images are co-registered and we have stacked the NDVI index layers of two dates in red, green, blue bands composite to perform a supervised classification. Secondly, sample pixels were manually selected from changed and unchanged area to be used in the training stage. Thirdly, for each pixel SVM produces a single output through its decision function, high detection overall accuracy (> 96%) and overall Kappa coefficient (> 0.89) were achieved using two Landsat images covering an 8-years period in study area. Lastly, SVM-based change detection with different kernel functions was compared using statistical evaluations.
作者：
Dengkui Mo;Hui Lin 0004;Jiping Li;Hua Sun 0002;Yujiu Xiong
期刊：
Data Science Journal,2007年6(SUPPL.):S445-S452 ISSN：1683-1470
通讯作者：
Mo, D.-K.(dengkuimo@yahoo.com.cn)
作者机构：
[Dengkui Mo; Hui Lin 0004; Hua Sun 0002] Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Shaoshan Road 498th, Changsha, Hunan, 410004, China;[Xiong, Yu-Mu] College of Resources Science and Technology, Beijing Normal University, Haidian District, Beijing, 100875, China;[Jiping Li] College of Resource and Environment, Central South University of Forestry and Technology, Shaoshan Road 498th, Changsha, Hunan, 410004, China
通讯机构：
[Mo, D.-K.] R;Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Shaoshan Road 498th, China
关键词：
High spatial resolution;Intelligent interpretation;Land cover;Mean shift;Remote sensing
摘要：
Very high spatial resolution remote sensing images have applications in many fields. However, research on the intelligent interpretation of such images is insufficient partly because of their the complexity and large size. In this study, a high spatial resolution remote sensing image intelligent interpretation system (HSR-RSIIIs) was designed with image segmentation, a geographical information system, and a data-mining algorithm. Some key methods such as image segmentation, feature extraction, feature selection, and classification algorithm for interpreting high spatial resolution remote sensing image have been studied. A land cover classification experiment was performed in the Zhuzhou area using a Quickbird multi-spectral image. The classification results were consistent with the visual interpretation results. In additional, the proposed interpretation method was compared with the traditional pixelbased method. The results indicate that the method proposed in the literature is more effective and intelligent than that used previously.
作者机构：
[莫登奎; 孙华; 林辉; 熊育久; 万玲凤] Research Centre of Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
通讯机构：
[Lin, H.] R;Research Centre of Remote Sensing and Information Engineering, , Changsha 410004, China
期刊：
PRINCIPLES AND PRACTICES OF DESERTIFICATION CONTROL, VOL I,2007年:399-407
通讯作者：
Mo D K
作者机构：
[Mo D K; Che F; Lin H; Xue X P; Sun H] Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.;[Li J P] Cent South Univ Forestry & Technol, Dept Resource & Environm, Changsha 410004, Hunan, Peoples R China.;[Xiong Y J] Beijing Normal Univ, Coll Resources Sci & Technol, Beijing 100875, Peoples R China.
通讯机构：
[Mo D K] C;Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China.
会议名称：
荒漠化控制科学技术国际大会
会议时间：
2006-10-14
会议地点：
中国北京
关键词：
rocky desertification;remote sensing;segmentation;object oriented image analysis;classification;Yunshun County
摘要：
Rocky desertification is a process of land degradation involving serious soil erosion,extensive exposure of basement rocks,drastic decrease in soil productivity,and the appearance of desert-like lands
摘要：
Remote sensing provides a useful source of data from which updated land cover information can be extraction for assessing and monitoring environment changes. This paper aims at achieving improved land cover classification performance based image segmentation and support vector machines (SVMs) classification. The object-based classification approach overcame the problem of salt-and-pepper effects found in classification results from traditional pixel-based approaches. The proposed method is a three-stage process, which makes use of the object information from neighboring pixels. Firstly, a robust image segmentation algorithm is used to achieve more homogeneous regions. Secondly, feature information is extracted from each segment and training samples is interactive selected in geographical information system platform. Thirdly, support vector machines classifier is employed to classify the land covers. The experimental results indicate that improved classification accuracy and smoother (more acceptable) is achieved compare with the traditional pixel-based method. Because of the image segmentation process significantly reduces the number of training samples, make SVMs classification method can be applied to information extraction from remotely sensed data.
摘要：
Image classification is an important technology in the application of remote sensing. Traditional methods of image classification are based on low or medium-resolution images, and the accuracy of classification is always very low. In recent years, high-resolution remote sensing images have significant improvements, but there is still no good method of classification. Studies showed that the accuracy of classified high-resolution images is even lower than that of low or medium -resolution images by traditional classification methods. This turns out that traditional classification technologies appeared to have serious error when using high-resolution images. In this paper, a method of multi-feature classification was introduced to high-resolution remote sensing image, thus avoiding the method of single-feature and pixel-based classification. In this method, pixel-based high-resolution images are changed into object-based images by segmentation. Models of area, perimeter, length, width, symmetry, ratio of length and width, rectangular fit and compactness were established to measure features of segmented objects. More over, the new method of using spectral and texture features to classify high-resolution images was completed. The result showed that the accuracy of image classification can be up to 91.6% by the multi-featured classification, which proved to have improved high-resolution remote sensing image classification.
摘要：
Remote sensing is a powerful tool for precision forestry, providing the forestry industry with spatial information on environment impacts, growth and yield, site variables and damage assessment. Nonetheless the extraction of information from remotely sensed imagery is presently labor intensive requiring highly qualified remote sensing experts, making this information source expensive and slow. With the improvement of spatial resolution, very high resolution remote sensing image are now a competitive alternative to aerial photography and field visits in forest resource survey. In recent years, numerous classification methods were described in the literature and they can be classified into two large classes: traditional pixel-based classification and object-oriented image analysis method. Traditional pixel-based classification techniques either supervised methods or unsupervised method all based on spectral analysis of individual pixels and significant progress has been achieved in recent years. However, these approaches have their limitations since the problem of mixed pixels is indeed reduced, but the internal variability and the noise within land cover classes are increased the improved spatial resolution. In order to improve the classification accuracy, object-oriented image analysis concept has been proposed. This paper explores the use of object oriented image analysis approaches in mapping forest resource and introduces a fast and robust segmentation algorithm - mean shift. The study is based on SPOT-5 image covering the national forest park of Tian'eshan, Zixing city, Hunan, China. Image processing included geometric and atmospheric correction and image segmentation and classification using spectral and spatial information to separate 5 classes 86. 5342% overall accuracy was achieved with this approach. In additional, object oriented image analysis method is compared with traditional pixel based method. The results show the importance, capabilities and challenges of object oriented approaches in providing detailed and accurate information about the physical structure of forest areas.
