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1.
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
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ZHENGShunyi ZHANGZuxun ZHANGJianqing 《地球空间信息科学学报》2004,7(4):243-248
In photogrammetry and remote sensing, image matching is a basic and crucial process for automatic DEM generation. In this paper we presented a image relaxation matching method based on feature points. This method can be considered as an extention of regular grid point based matching. It avoids the shortcome of grid point based matching. For example, with this method, we can avoid low or even no texture area where errors frequently appear in cross correlaton matching. In the mean while, it makes full use of some mature techniques such as probability relaxation, image pyramid and the like which have already been successfully used in grid point matching process. Application of the technique to DEM generaton in different regions proved that it is more reasonable and reliable. 相似文献
4.
SHIWenzhong AhmedShaker 《地球空间信息科学学报》2004,7(1):24-30
The exploitation of different non-rigorous mathematical models as opposed to the satellite rigorous models is discussed for geometric corrections and topographic/thematic maps production of high-resolution satellite imagery (HRSI). Furthermore, this paper focuses on the effects of the number of GCPs and the terrain elevation difference within the area covered by the images on the obtained ground points accuracy. From the research, it is obviously found that non-rigorous orientation and triangulation models can be used successfully in most cases for 2D rectification and 3D ground points determination without a camera model or the satellite ephemeris data. In addition, the accuracy up to the sub-pixel level in plane and about one pixel in elevation can be achieved with a modest number of GCPs. 相似文献
5.
CHENChujiang LIDeren ZHUQing 《地球空间信息科学学报》2005,8(1):33-38
This paper presents the studies of the refining of IKONOS-2 RPC, the transform of the datum, the mode of the control point distribution and the method of IKONOS stereo triangulation, so that IKONOS imagery can be used to collect the precise geospatial data and produce the large scale map. The transform between the IKONOS-2 image space and the national coordinate system based on the RPC have been developed, and the results of block adjustment with various control schemes in a practical project near Himalayas have been examined and analysed. The encouraging results of high positioning accuracy have been obtained. 相似文献
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LIUChun SHIWenzhong LIUDajie 《地球空间信息科学学报》2003,6(2):13-19
On the basis of the principles of simple random sampling,the statistical model of rate of disfigure-ment(RD)is put forward and described in detail.According to the definition of simple random sampling for the attribute data in GIS,the mean and variance of the RD are deduced as the characteristic value of the statistical model in order to explain the feasibility of the accuracy measurement of the attribute data in GIS by using the RD.Moreover,on the basis of the mean and variance of the RD,the quality assessment method for attribute data of vector maps during the data collecting is discussed.The RD spread graph is also drawn to see whether the quality of the attribute data is under control.The RD model can synthetically judge the quality of attribute data,which is different from other measurement coefficients that only discuss accuracy of classification. 相似文献
7.
ZHAO Xi PANG Xiaoping ZHANG Shengli ZHANG Xiang 《地球空间信息科学学报》2007,10(1):75-78
True color image city map is a sort of new-style map which combines the high resolution image and map symbols and shows both advantages in visualization. At the same time, the map unification and harmonization should be taken into account dur-ing the design process, since some visual conflicts appear when map symbols overlaid on the true color image. The objective of this research is to explore the rules in the process of true color image city map design based on chromatic and aesthetic knowledge. At the end, taking the Image Atlas of Guangzhou as an example, image color adjustment, road network presentation, and symbol de-signing issues will be discussed in the application. 相似文献
8.
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
9.
张煜 《地球空间信息科学学报》2008,11(1):38-42
A texture image segmentation based on nonlinear diffusion is presented. The scale of texture can be measured during the process of nonlinear diffusion. A smooth 5-channel vector image with edge preserved, which is composed of intensity, scale and orientation of texture image, can be achieved by coupled nonlinear diffusion. A multi-channel statistical region active contour is employed to segment this vector image. The method can be seen as a kind of unsupervised segmentation because parameters are not sensitive to different texture images. Experimental results show its high efficiency in the semiautomatic extraction of texture image. 相似文献
10.
ZHANG Jianqing ZHANG Zuxun FANG Zhen FAN Hong ZHANG Jianqing Professor National Laboratory 《地球空间信息科学学报》1999,2(1):16-20
Because of quick development of cities, the update of urban GIS data is very important. Change detection is the base of automatic or semi-automatic data update. One way of change detections in urban area is based on old and new aerial images acquired in different durations. The corresponding theory and experiments are introduced and analyzed in this paper. The main procedure includes four stages. The new and old images have to be registered firstly. Then image matching, based on the maximum correlation coefficient, is performed between registered images after the low contrast areas have been removed. The regions with low matching quality are extracted as candidate changed areas. Thirdly, the Gaussian-Laplacian operator is used to detect edges in candidate changed areas on both the registered images, and the straight lines are detected by Hough transformation. Finally, the changed houses and roads can be detected on the basis of straight line matching in candidate changed areas between registered images. 相似文献
11.
In this study, we test the use of Land Use and Coverage Area frame Survey (LUCAS) in-situ reference data for classifying high-resolution Sentinel-2 imagery at a large scale. We compare several pre-processing schemes (PS) for LUCAS data and propose a new PS for a fully automated classification of satellite imagery on the national level. The image data utilizes a high-dimensional Sentinel-2-based image feature space. Key elements of LUCAS data pre-processing include two positioning approaches and three semantic selection approaches. The latter approaches differ in the applied quality measures for identifying valid reference points and by the number of LU/LC classes (7–12). In an iterative training process, the impact of the chosen PS on a Random Forest image classifier is evaluated. The results are compared to LUCAS reference points that are not pre-processed, which act as a benchmark, and the classification quality is evaluated by independent sets of validation points. The classification results show that the positional correction of LUCAS points has an especially positive effect on the overall classification accuracy. On average, this improves the accuracy by 3.7%. This improvement is lowest for the most rigid sample selection approach, PS2, and highest for the benchmark data set, PS0. The highest overall accuracy is 93.1% which is achieved by using the newly developed PS3; all PS achieve overall accuracies of 80% and higher on average. While the difference in overall accuracy between the PS is likely to be influenced by the respective number of LU/LC classes, we conclude that, overall, LUCAS in-situ data is a suitable source for reference information for large scale high resolution LC mapping using Sentinel-2 imagery. Existing sample selection approaches developed for Landsat imagery can be transferred to Sentinel-2 imagery, achieving comparable semantic accuracies while increasing the spatial resolution. The resulting LC classification product that uses the newly developed PS is available for Germany via DOI: https://doi.org/10.15489/1ccmlap3mn39. 相似文献
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Multi-temporal aerial imagery captured via an approach called repeat station imaging (RSI) facilitates post-hazard assessment of damage to infrastructure. Spectral-radiometric (SR) variations caused by differences in shadowing may inhibit successful change detection based on image differencing. This study evaluates a novel approach to shadow classification based on bi-temporal imagery, which exploits SR change signatures associated with transient shadows. Changes in intensity (brightness from red–green–blue images) and intensity-normalized blue waveband values provide a basis for classifying transient shadows across a range of material types with unique reflectance properties, using thresholds that proved versatile for very different scenes. We derive classification thresholds for persistent shadows based on hue to intensity ratio (H/I) images, by exploiting statistics obtained from transient shadow areas. We assess shadow classification accuracy based on this procedure, and compare it to the more conventional approach of thresholding individual H/I images based on frequency distributions. Our efficient and semi-automated shadow classification procedure shows improved mean accuracy (93.3%) and versatility with different image sets over the conventional approach (84.7%). For proof-of-concept, we demonstrate that overlaying bi-temporal imagery also facilitates normalization of intensity values in transient shadow areas, as part of an integrated procedure to support near-real-time change detection. 相似文献
14.
Olivier R. van Lier Richard A. Fournier Robert L. Bradley Nelson Thiffault 《International Journal of Applied Earth Observation and Geoinformation》2009,11(5):334-343
Invasive ericaceous shrubs (e.g. Kalmia angustifolia, Rhododendron groenlandicum, Vaccinium spp.) may reduce the regeneration and early growth of black spruce (Picea mariana) seedlings, the most economically important boreal tree species in Quebec. Our study focused, therefore, on developing a method for mapping ericaceous shrubs from satellite images. The method integrates very high resolution satellite imagery (IKONOS) to guide classifiers applied to medium resolution satellite imagery (Landsat-TM). An object-oriented image classification approach was applied using Definiens eCognition software. An independent ground survey revealed 80% accuracy at the very high spatial resolution. We found that the partial use (70%) of classified polygons derived from the IKONOS images were an effective way to guide classification algorithms applied to the Landsat-TM imagery. The results of this latter classification (78.4% overall accuracy) were assessed by the remaining portion (30%) of unused very high resolution classified polygons. We further validated our method (65.5% overall accuracy) by assessing the correspondence of an ericaceous cover classification scheme done with a Landsat-TM image and results of our ground survey using an independent set of 275 sample plots. Discrimination of ericaceous shrub cover from other land cover types was achieved with precision at both spatial resolutions with producer accuracies of 87.7% and 79.4% from IKONOS and Landsat, respectively. The method is weaker for areas with sparse cover of ericaceous shrubs or dense tree cover. Our method is adapted, therefore, for mapping the spatial distribution of ericaceous shrubs and is compatible with existing forest stand maps. 相似文献
15.
I. K. Lur'ye 《地理信息系统科学与遥感》2013,50(3):255-261
Algorithms, designed for digital image processing in standard mainframe computers and representing sequential stages in a land-use classification procedure, are used to produce maps of agricultural crop types from multispectral satellite imagery. Pixel reflectance values are first grouped according to an unsupervised “rapid classification algorithm,” or data compression procedure. Mean reflectance values of the resulting classes then go into a supervised “sequential clustering algorithm” where classes are refined according to training value and other parameter inputs. The objective is to increase the accessibility of automated image interpretation while balancing classification accuracy and processing time. Translated from: Vestnik Moskovskogo Universiteta, geografiya, 1984, No. 4, pp. 63-69. 相似文献
16.
《地理信息系统科学与遥感》2013,50(3):237-250
The use of remotely sensed imagery to generate land cover models is common today. Validation of these models typically involves the use of an independent set of ground-truth data that are used to calculate an error matrix resulting in estimates of omission, commission, and overall error. However, each estimate of error contains a degree of uncertainty itself due to: (1) conceptual bias; (2) location/registration and co-registration errors; and (3) variability in the sample sites used to produce and validate the model. In this study, focus was not placed upon describing land cover mapping techniques, but rather the application of bootstrap resampling to improve the characterization of classification error, demonstrate a method to determine uncertainty from sample site variability, and calculate confidence limits using statistical bootstrap resampling of 500 sample sites acquired within a single Landsat 5 TM image. The sample sites represented one of five land cover categories (water, roads, lava, irrigated agriculture, and rangelands), with each category containing 100 samples. The sample set was then iteratively resampled (n = 200) and 65 sites were randomly selected (without replacement) for use as classification training sites, while the balance (n = 35) were used for validation. Imagery was subsequently classified using a maximum likelihood technique and the model validated using a standard error matrix. This classification-validation process was repeated 200 times. Confidence intervals were then calculated using the resulting omission and commission errors. Results from this experiment indicate that bootstrap resampling is an effective method to characterize classification uncertainty and determine the effect of sample bias. 相似文献
17.
遥感信息处理不确定性的可视化表达 总被引：2，自引：0，他引：2
如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布方式,是遥感信息不确定性研究的关键问题之一.传统的度量方法(例如误差矩阵)是将以训练样本集为基础的度量作为总分类精度的度量,而我们需要估计模型对于"样本外数据"的性能.本文首先利用信息论和粗糙集理论等度量遥感分类影像属性信息的不确定性,提出基于像元、目标和影像的遥感信息不确定性度量指标;然后分别描述了基于不同度量指标的可视化表达方式,并对我国黄河三角洲地区的Landsat TM影像进行了分类信息不确定性度量和可视化表达实验. 相似文献
18.
提出了一种基于粗集属性划分的遥感分类新方法, 构造了基于粗集的集成遥感分类器。该分类器利用粗集理论将输入的属性集合划分为多个约减, 利用这些约减构造多个训练子集。每个训练子集训练神经网分类器, 在决策时将多个单个分类器的结果进行投票选举。这种方法即减少了单个分类器的输入属性个数, 又避免了由于属性选取造成单一分类器在某些分类上的错误偏见。该分类器与神经网分类器方法, 以及属性选取与神经网结合方法进行了比较。结果表明RSEC无论在分类精度上, 还是在不同样本个数条件下的精度稳定程度上均有较好表现。 相似文献
19.
A basic methodology for land cover classification using airborne multispectral scanner (MSS) imagery is outlined. This includes waveband selection and radiometric calibration; correction for scan angle and atmosphere; training and classification and accuracy assessment. Refinements to this basic methodology include per‐field sampling and the addition of low‐pass filtering, image texture, prior probabilities and two dates of imagery. For a study area in upland England, eight land covers were classified with a mean accuracy of 52.6 percent using the basic methodology. This was increased to 79.0 percent by using a suitability refined methodology. Per‐field sampling accounted for the largest proportion of this increase. 相似文献
20.
Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5 m?2) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. Objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata. 相似文献