共查询到20条相似文献，搜索用时 750 毫秒
1.
MWRI (MicroWave Radiation Imager) is one of the payloads on our next generation polar meteorological satellite FY-3. MWRI conically scans with a fixed incident angle on the earth surface. It is the first time for Chinese remote sensor to use this scan mode. In this work, we present a geolocation method for FY-3 MWRI’s remote sensing image based on its special scan geometry. The integrated coordinate systems and the specific relationships with these coordinate systems are defined. A spatial relationship model between the remote sensing data and the earth-based coordinate system is established. This method also includes an algorithm of satellite orbit computation, which is used to get the satellite’s instantaneous velocity vector from its position. This method has been applied to MWRI’s remote sensing image geolocation. The results show that the accuracy of this method can achieve 1 pixel. The 33 GCPs (Ground Control Points) which are in the regiones of FY-3 MWRI’s observation have been collected and used to analyze the precision of the geolocation. By statistical analysis, the error along-track is about 1.5km, and the error along-scan is about 3.0km. It is obvious that this method fulfills the requirement of precision for FY-3 MWRI whose space resolution exceeds 5km. 相似文献
2.
ZHENGZhaobao 《地球空间信息科学学报》2002,5(1):1-6
In the photogrammetry,some researchers have applied genetic algorithms in aerial image texture classification and reducing hyper-spectrum remote sensing data.Genetic algorithm can rapidly find the solutions which are close to the optimal solution.But it is not easy to find the optimal solution.In order to solve the problem,a cooperative evolution idea integrating genetic algorithm and ant colony algorithm is presented in this paper.On the basis of the advantages of ant colony algorithm,this paper proposes the method integrating genetic algorithms and ant colony algorithm to overcome the drawback of genetic algorithms.Moreover,the paper takes designing texture classification masks of aerial images as an example to illustrate the integration theory and procedures. 相似文献
3.
Data mining techniques are used to discover knowledge from GIS database in order to improve remote sensing image classification.Two learning granularities are proposed for inductive learning from spatial data,one is spatial object granularity,the other is pixel granularity.We also present an approach to combine inductive learning with conventional image classification methods,which selects class probability of Bayes classification as learning attributes.A land use classification experiment is performed in the Beijing area using SPOT multi-spectral image and GIS data.Rules about spatial distribution patterns and shape features are discovered by C5.0 inductive learning algorithm and then the image is reclassified by deductive reasoning.Comparing with the result produced only by Bayes classification,the overall accuracy increased by 11% and the accuracy of some classes,such as garden and forest,increased by about 30%.The results indicate that inductive learning can resolve spectral confusion to a great extent.Combining Bayes method with inductive learning not only improves classification accuracy greatly,but also extends the classification by subdividing some classes with the discovered knowledge. 相似文献
4.
Bayesian and Geostatistical Approaches to Combining Categorical Data Derived from Visual and Digital Processing of Remotely Sensed Images 总被引：1，自引：0，他引：1
ZHANGJingxiong LIDeren 《地球空间信息科学学报》2005,8(2):90-97
This paper seeks a synthesis of Bayesian and geostatistical approaches to combining categorical data in the context of remote sensing classification. By experiment with aerial photographs and Landsat TM data, accuracy of spectral, spatial, and combined classification results was evaluated. It was confirmed that the incorporation of spatial information in spectral classification increases accuracy significantly. Secondly, through test with a 5-class and a 3-class classification schemes, it was revealed that setting a proper semantic framework for classification is fundamental to any endeavors of categorical mapping and the most important factor affecting accuracy. Lastly, this paper promotes non-parametric methods for both definition of class membership profiling based on band-specific histograms of image intensities and derivation of spatial probability via indicator kriging, a non-parametric geostatistical technique. 相似文献
5.
Spatial clustering is widely used in many fields such as WSN （Wireless Sensor Networks）, web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy： SSPG （Single-Shortest-Path Graph）. With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient. 相似文献
6.
Neural network based on rough sets and its application to remote sensing image classification 总被引：2，自引：0，他引：2
WUZhaocong LIDeren 《地球空间信息科学学报》2002,5(2):17-21
This paper presents a new kind of back propagation neural network(BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi-spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach. 相似文献
7.
ZHOU Jiaogen GUAN Jihong LI Pingxiang 《地球空间信息科学学报》2007,10(2):137-144
Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clus- tering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient. 相似文献
8.
ZHUGuobin DanG.Blumberg 《地球空间信息科学学报》2004,7(4):249-254
This paper introduces an advanced method based on remote sensing and Geographic Information System for urban open space extraction combining spectral and geometric characteristics. From both semantic and remote sensing perspectives, a hybrid hierarchy structure and class organization of open space are issues and mapped from one to another. Based on per-pixel and segmentation mechanism separately, two classification approaches are performed. Owing to prior of spatial aggregation and spectral contribution, the segmentation-based classification exhibits its superiority over a pixel-based classification. Finally a GIS-based post procedure is hired to eliminate some unsuitable open space components in both spatial and numerical constraints on the one hand, and separate open space some fabrics from fused remote sensing classes by defining their Shape Index on the other hand. The case study of Beer Sheva based on ASTER data proves this method is a feasible way for open space extraction. 相似文献
9.
空间数据模糊聚类的有效性(英文) 总被引：1，自引：0，他引：1
The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the uncertainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices. 相似文献
10.
ZHUQing TIANYixiang 《地球空间信息科学学报》2005,8(2):85-89
The methodology of catchment extraction especially from regular grid digital elevation models (DEMs) is briefly reviewed. Then an efficient algorithm, which combines vector process and traditional neighbourhood raster process, is designed for extracting the catchments and subcatchments from depressionless DEMs. The catchment area of each river in the grid DEM data is identified and delineated, then is divided into subcatchments as required. Compared to traditional processes, this method for identifying catchments focuses on the boundaries instead of the area inside the catchments and avoids the boundary intersection phenomena. Last, the algorithm is tested with a set of DEMs of different sizes, and the result proves that the computation efficiency and accuracy are better than existent methods. 相似文献
11.
一种模糊聚类的遥感影像分析方法研究 总被引：1，自引：0，他引：1
针对传统模糊聚类的遥感影像分析方法的不足,重点研究基于模糊ISODATA聚类的遥感影像分析。通过Matlab软件编程实现基于迭代自组织数据分析技术、模糊C均值聚类、模糊ISODATA算法对合成图像、纹理图像及真实遥感影像的分类,并对其分类结果进行讨论。通过实验数据对比,评价FISODATA算法的优越性。实验结果表明:ISODATA算法及FISODATA算法都能够实现变类,而FCM算法只能在固定聚类数下进行分类,但是,ISODATA算法分类机制不稳定,不能每次都确定正确聚类数。在迭代过程中,将FISODATA算法引入模糊集理论,便能够快速准确的实现聚类数的确定。 相似文献
12.
复杂环境下高分二号遥感影像的城市地表水体提取 总被引：1，自引：0，他引：1
水体指数可以抑制背景噪声和提高地表水体的可分性,已经广泛用于地表水体提取。传统FCM聚类算法考虑了地物的不确定性,但没有顾及地物的邻域空间信息,对背景异质性比较敏感。针对传统FCM聚类算法的不足,提出一种可变邻域的区域FCM聚类算法。由于复杂环境下高分二号(GF-2)遥感影像的城市地表水体具有复杂异质背景和不确定性的特点,本文利用水体指数和区域FCM聚类算法的优点,提出一种整合水体指数和区域FCM的城市地表水体自动提取算法,该算法主要步骤包括:(1)去除影像阴影后计算归一化差分水体指数NDWI(Normalized Difference Water Index);(2)区域FCM聚类算法;(3)整合水体指数和区域FCM聚类的城市地表水体自动提取算法。最后采用两景GF-2高分辨率遥感影像(广州和武汉)进行实验,验证了该算法的有效性,并与经典地表水体提取算法进行对比分析。实验结果表明:该算法具有较高的水体提取精度,城市地表水体边界既具有较好的区域完整性又保持了局部细节,同时对城市地表水体复杂背景噪声具有较好的抑制作用,有效减少传统FCM聚类算法的"胡椒盐"现象。 相似文献
13.
高分辨率遥感影像超像素的模糊聚类分割法 总被引：1，自引：0，他引：1
传统模糊C均值聚类在影像分割中只考虑影像的灰度特征,导致该算法用于高空间分辨率遥感影像分割时分割结果不理想。针对该问题,本文提出了一种高分辨率遥感影像超像素的模糊聚类分割方法。该方法首先利用分水岭变换算法产生多个超像素子区域;然后比较各个子区域间光谱特征的相似性;最后利用融合光谱特征的模糊C均值聚类对这些超像素子区域进行合并。试验选用4组不同场景的遥感影像,采用定性和定量相结合的方法评价试验结果。试验结果表明,该方法有效提高了分割区域的分割精度,并取得了较好的分割视觉效果。 相似文献
14.
为了弥补蝙蝠算法后期收敛速度慢、寻优精度不高、易陷入局部最优值的缺点,本文提出了一种新的遥感图像分类算法--GABA算法,该算法将遗传算法中的选择、交叉、变异操作应用到蝙蝠算法中,使蝙蝠算法具有变异机制,避免种群个体陷入局部最优,提高了算法全局寻优能力,增加了蝙蝠算法的多样性。同时,为了突出本文算法的优点,试验将蝙蝠算法、K-means算法、粒子群算法与本文算法结果进行比较,分析评价遥感图像的分类结果。试验表明本文算法在遥感图像分类应用中既提高了分类精度又减少了分类时间,是一种可行、有效的遥感图像分类方法。 相似文献
15.
模糊C均值聚类是一种经典的非监督聚类模型,成功地应用于遥感影像分类。但是该方法对初始值敏感,容易陷入局部最优解;同时聚类时仅考虑光谱信息,忽略了空间信息。本文提出了一种新的基于马尔科夫随机场的模糊C均值聚类方法,该方法把马尔科夫随机场和模糊C均值结合在一起。初始值依据第一主成分的密度函数确定,这样克服了对初始值的依赖性,又在聚类的时候考虑了空间信息。通过实例数据验证,所提出的方法分类精度优于传统的模糊C均值模型。 相似文献
16.
Chengjie Zhu Shizhi Yang Qiang Zhao Shengcheng Cui Nu Wen 《Journal of the Indian Society of Remote Sensing》2014,42(1):35-49
Fuzzy c-means (FCM) algorithm is a popular method in image segmentation and image classification. However, the traditional FCM algorithm cannot achieve satisfactory classification results because remote sensing image data are not subjected to Gaussian distribution, contain some types of noise, are nonlinear, and lack labeled data. This paper presents a robust semi-supervised kernel-FCM algorithm incorporating local spatial information (RSSKFCM_S) to solve the aforementioned problems. In the proposed algorithm, insensitivity to noise is enhanced by introducing contextual spatial information. The non-Euclidean structure and the problem in nonlinearity are resolved through kernel methods. Semi-supervised learning technique is utilized to supervise the iterative process to reduce step number and improve classification accuracy. Finally, the performance of the proposed RSSKFCM_S algorithm is tested and compared with several similar approaches. Experimental results for the multispectral remote sensing image show that the RSSKFCM_S algorithm is more effective and efficient. 相似文献
17.
为了提高遥感影像地物分类精度,提出了一种基于多特征组合与自动加权K-Means聚类算法的影像分类方法。首先提取影像的SIFT,GIST,颜色,Census和Gabor等多种类型特征,然后通过实验分析确定最佳特征组合。针对一般K-Means算法没有考虑各个特征值权重的问题,提出利用自动加权K-Means算法计算不同特征分量的权值,分别对SIFT,GIST和Gabor特征构建了基于权重的影像特征词汇表;然后利用稀疏编码算法进行影像编码;最后使用SVM算法完成影像分类。通过实验表明提出的方法能有效提高遥感影像分类准确性,并且具有较好的稳定性和鲁棒性。 相似文献
18.
为提高遥感影像分类精度,本文提出基于混沌遗传算法(Chaos Genetic Algorithm)的遥感影像分类方法.首先应用混沌遗传算法对样本进行自学习得到全局最优的聚类中心,然后通过得到的聚类中心对整幅影像进行分类.该方法利用混沌变量的遍历性,进行粗粒搜索,优化遗传算法的初始种群,从而提高收敛速度;对经过选择算子、... 相似文献
19.