共查询到20条相似文献，搜索用时 15 毫秒
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LUO Chengfeng LIU Zhengjun YAN Qin 《地球空间信息科学学报》2007,10(2):124-127
A fuzzy ARTMAP classifier is adopted for a classification experiment of CBERS-2 imagery. The fundamental theory and processing about the algorithm are first introduced, followed with a land-use classification experiment in Shihezi County on CBERS-2 high resolution imagery. Three classifiers are compared: maximum likelihood classifier (MLC), error back propagation (BP) classifier, and fuzzy ARTMAP classifier. The comparison shows comparably better results for the fuzzy ARTMAP classifier, with overall classification accuracy of 9.9% and 4.6% higher than that of MLC and BP. The results also prove that the fuzzy ARTMAP classifier has better discernment in identifying bare soil on CBERS-2 imagery. 相似文献
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用模糊ARTMAP算法对CBERS-2数据进行分类 总被引：3，自引：0，他引：3
用模糊ARTMAP（fuzzy adaptive resonance theorymap）神经网络算法对CBERS-2数据进行了分类实践。首先介绍了模糊ARTMAP神经网络的算法原理和具体训练分类过程；然后用2004年9月新疆石河子地区的影像数据进行土地利用分类试验，并将分类结果与基于统计的最大似然法（MLC）、反向传播神经网络（BP）的分类结果作比较，总分类精度比MLC和BP算法分别提高9．9％和4．6％。结果表明，模糊ARTMAP对试验区CBERS-2影像上的裸地识别能力很强，对高分辨率的CBERS-2影像可获得很好的分类结果。 相似文献
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提出了一种基于自适应谐振理论建立起来的自组织模糊ARTMAP神经网络分类器。分析了ART神经网络的结构和工作原理,给出模糊ARTMAP神经网络分类的具体算法,并将其运用到TM遥感影像分类的实验中。结果表明模糊ARTMAP神经网络分类器的速度快,精度高,比常用的BP网络具有更好的性能。 相似文献
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Mehdi Azari Mohsen Ahadnejad Reveshty 《Journal of the Indian Society of Remote Sensing》2013,41(4):993-1008
Urban growth is the result of physical and human impacts. In this study Cellular Automata (CA) has been used to analyze physical suitability and human forces in urban growth modelling of Maraghe. The multi-temporal satellite imagery, physical suitability and human impacts Layers have been applied to the modelling. In order to evaluate the accuracy of the image classification methods, Fuzzy ARTMAP is compared with Maximum Likelihood Classification (MLC) and Minimum Distance Classification (MDC) methods. The image classification results showed an overall accuracy of 93 %. Therefore, it is employed for classification of multi-temporal satellite imagery. In order to weight physical suitability and human impacts layers or geographical transition rules in the modelling, regression analysis, the correlation coefficient, trial-and-error method and visual comparison used. The statistical methods are presented to validate neighbourhood scales in the urban growth modelling. The calibration of the model is in fact to the estimate value of the physical suitability and human impacts layer (combinatory layer of demand for urban land and the government facilities) in the modelling. The results obtained from the model calibration showed that human impacts have the highest influence in the urban growth among other factors. Also a small neighbourhood scale (25:5?×?5 cells) is more realistic in the modeling. The accuracy of final validation is 83 % and the final scenario is based on this validation. A fuzzy CA has been used in urban growth modeling of Maraghe. The final scenario shows that Maraghe will growth on the east side, where the land demand for built up area and government facilities plays the significant role. 相似文献
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Vishakha Sood Sheifali Gupta Hemendra Singh Gusain Sartajvir Singh 《Journal of the Indian Society of Remote Sensing》2018,46(12):1991-2002
In recent years, the significant increase in research on spatial information is observed. Classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier; (b) k-mean clustering as unsupervised classifier; and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5–88.5%) as compared to MLC (81–84%) and k-mean (74–81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography. 相似文献
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Ming Shang Shi-Xin Wang Yi Zhou Cong Du 《Journal of the Indian Society of Remote Sensing》2018,46(9):1333-1340
In this study, we used Landsat-8 imagery to test object- and pixel-based image classification approaches in an urban fringe area. For object-based classification, we applied four machine learning classifiers: decision tree (DT), naive Bayes (NB), random trees (RT), and support vector machine (SVM). For pixel-based classification, we utilized the maximum likelihood classifier (MLC). Specifically, we explored the influence of repeated sampling on classification results with different training sample sizes. We found that (1) except the overall accuracy of NB, those of the other four classifiers increased as the training sample size increased; (2) repeated sampling had a significant effect on classification accuracy, especially for the DT and NB classifiers; and (3) SVM achieved the best classification accuracy. In addition, the performance of the object-based classifiers was superior to that of the pixel-based classifier. The results of this study can provide guidance on the training sample size and classifier selection. 相似文献
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自适应模糊规则分类方法及在TM土地覆盖分类中的应用研究 总被引：1，自引：1，他引：0
根据自组织网络和模糊逻辑推理,实现土地覆盖自适应模糊规则分类方法。该方法通过网络的节点和权值提取出模糊规则,调整网络中节点个数(即相应增加规则节点数)和权值向量,使模糊规则自动生成,并利用模糊逻辑推理,完成TM土地覆盖分类。对拒分类的像元,自适应增加K值使其可分。该方法所得分类精度及Kapp系数与最大似然分类方法结果相比分别提高了2.7%和2.9%;与自组织网络相比,总精度相差不大,而Kapp系数低1%。实验证明,如何提取和表示非光谱知识,从而解决类别混淆等问题,是提高自适应模糊规则分类性能的关键 相似文献
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Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High Fβ values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar Fβ values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards. 相似文献
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基于决策树的CBERS遥感影像分类及分析评价 总被引：1，自引：0，他引：1
以江苏省徐州市为研究区,以城市土地利用遥感分类为目标,采用CBERS多光谱数据的近红外波段、全球环境监测植被指数(GEMI)、归一化植被指数(NDVI)及主成分分析得出的第一和第二主成分作为分类的特征数据,基于先验知识和统计分析构建层次分类决策树,进而发展和改进了决策树交互式构建算法,实现了城市土地利用遥感分类。通过与最大似然分类器(MLC)和支持向量机分类器(SVM)分类结果的比较分析,表明基于多种特征的决策树分类器能够有效应用于CBERS遥感数据分类,在研究区具有良好的推广性。 相似文献
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利用高光谱遥感影像的空间纹理特征,可以提高高光谱遥感影像的分类精度。提出了一种多层级二值模式的高光谱影像空-谱联合分类方法。该方法将高光谱影像转化为局部二值模式特征图像获取像元微观特征,基于特征图像生成多层级特征向量获取像元宏观特征。为验证该方法的有效性,选取PaviaU、Salinas和Chikusei高光谱影像数据,利用核极限学习机分类器,分别针对光谱、局部二值模式、多层级二值模式等特征开展实验。结果表明,多层级二值模式空-谱分类总体精度分别达到97.31%、98.96%和97.85%,明显优于传统光谱、3Gabor空-谱等分类方法。该方法可为高光谱影像分类提供更加有效的类别判定特征,有助于提高影像分类精度并获取更加平滑的分类结果图。 相似文献
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Wenzhong Shi Kimfung Liu Hua Zhang 《International Journal of Applied Earth Observation and Geoinformation》2011
The multiple classifier system (MCS) is an effective automatic classification method, useful in connection with remote sensing analysis techniques. Combining MSC with induced fuzzy topology enables a decomposition of image classes. This fuzzy topological MCS then provides a new and improved approach to classification. The basic classification methods discussed in this paper include maximum likelihood classification (MLC), minimum distance classification (MIND) and Mahalanobis distance classification (MAH). 相似文献
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Manoj Joseph S. Rama Subramoniam K. S. Srinivasan Suparn Pathak J. R. Sharma 《Journal of the Indian Society of Remote Sensing》2013,41(1):177-182
The potential of quad polarization radar data for the target discrimination has been analyzed. Quad polarization data of the RADARSAT-2 fine resolution mode has been utilized. Class separability analysis has been carried out on different polarization combinations using Transformed Divergence (TD) method and it is observed that HH-HV/VH-VV polarization combination gives better class separability when compared to other polarization combinations. Classification has been carried out on the optimized polarization combination using Maximum likelihood (MLC) and Support Vector Machine (SVM) classifiers. It is observed that SVM classification gives better classification accuracy compared to MLC. Overall classification accuracy is 93.03% for SVM and 88.78% for MLC. Class separability and classification accuracy comparison results are presented. 相似文献
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Comparison of the Different Classifiers in Vegetation Species Discrimination Using Hyperspectral Reflectance Data 总被引：1，自引：0，他引：1
Yuanyong Dian Shenghui Fang Yuan Le Yongrong Xu Chonghuai Yao 《Journal of the Indian Society of Remote Sensing》2014,42(1):61-72
Feature selection methods play an important role in Hyperspectral Remote Sensing applications, especially in classification. This paper proposed a new Feature selection strategy for Hyperspectral dataset. This strategy was designed to help refine vegetation classification of 4 categories with 13 species vegetation which are the most common species in central China. An ASD field spectrometer (Analytical Spectral Device) was used to collect spectrum information of plant leaves from each species through 400 nm to 900 nm with 1 nm spectral resolution. Firstly, correlation between the physical/chemical characteristics of the leaves and the separability of each vegetation species was tested. Then, two feature selection methods, spectral angle and spectral distance, and the feature parameters extracted from spectral curves (FPESC) were used to build the feature space which would be the input space for the classifiers. At last, two linear classifiers, mahalanobis distance (MDC), and fisher linear discriminate analysis (FLDA), and a quadratic classifier, maximum likelihood (MLC), were used for vegetation species refine classification. The results showed that (1) there were no significant differences among 13 species on the leaf dry weight (physical parameter) and leaf chlorophyll content (chemical parameter); (2) FPESC of 13 species have distinctive differences and could be ideal features to discriminate these species; (3) The linear classifiers, MDC and FLDA, have better classification results in the experiments compared to the quadratic classifier MLC, where MDC has the highest classification accuracy which is above 96.2 %. 相似文献
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本文在研究BP神经网络和模糊理论的基础上,提出了传统BP算法的一种改进方法和基于模糊系统的神经网络遥感影像分类方法。通过试验表明:基于模糊技术的神经网络分类方法要优于BP神经网络方法,取得了令人满意的效果。 相似文献
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云类识别是实现卫星云图自动分析的基础,针对卫星云图易受噪声干扰且不同云系往往相互交叠的特点,构造一种面向云类识别的自适应模糊支持向量机。该方法不仅改进了隶属度函数的表现形式,而且通过定义控制临界隶属度和隶属度衰减趋势的参数,使隶属度能根据不同云系样本的具体分布特性自适应调整,解决了传统模糊支持向量机的隶属度函数难以反映样本分布的问题。在MTSAT卫星云图上的实验结果表明,通过提取云图可见光通道的反照率、红外通道的亮温及三种亮温差作为云图的光谱特征,并结合统计纹理特征,所构造的自适应模糊支持向量机分类器能有效区分晴空区、低云、中云、高云及直展云;云类识别准确率优于标准支持向量机和传统模糊支持向量机,且具有更强的稳定性和自适应性。 相似文献
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雷达遥感图像分类新技术发展研究 总被引：6，自引：0，他引：6
总结了雷达遥感图像分类技术的发展过程,指出新的分类技术正朝着采用新特征(如雷达极化信息与干涉信息、多参数极化干涉信息、多时相信息、DEM与地理信息等),应用新理论(如小波理论、分形理论、模糊理论),设计新算法(如改进的最大似然法、上下文分类法、改进的神经网络分类算法等)的方向发展. 相似文献