共查询到20条相似文献，搜索用时 27 毫秒
1.
Sujay Dutta S.A. Sharma A.P. Khera Ajai M. Yadav R.S. Hooda K.E. Mothikumar M.L. Manchanda 《ISPRS Journal of Photogrammetry and Remote Sensing》1994,49(6)
The accuracy of cotton crop classification using satellite data has been assessed with respect to a detailed land cover map prepared by field survey. The effect of spatial resolution on classification accuracy was studied using LISS-I (spatial resolution 72.6 m) and LISS-II data (spatial resolution 36.25 m) of the Indian remote sensing satellite IRS-1B. The performances of the maximum likelihood and the minimum distance to mean as classifiers have also been assessed. LISS-II data have been found to give a higher classification accuracy. The estimate of cotton acreage using LISS-II data was closer to that obtained from the base map. The maximum likelihood classifier (MXL) and the minimum distance to mean (MDM) classifier performed equally well. 相似文献
2.
优化子空间SVM集成的高光谱图像分类 总被引：2，自引：0，他引：2
随机子空间集成是很有前景的高光谱图像分类技术,子空间的多样性和单个子空间的性能与集成后的分类精度密切相关。传统方法在增强单个子空间性能的同时,往往会获得大量最优但相似的子空间,因而减小它们之间的多样性,限制集成系统的分类精度。为此,提出优化子空间SVM集成的高光谱图像分类方法。该方法采用支持向量机(SVM)作为基分类器,并通过SVM之间的模式差别对随机子空间进行k-means聚类,最后选择每类中J-M距离最大的子空间进行集成,从而实现高光谱图像分类。实验结果显示,优化子空间SVM集成的高光谱图像分类方法能够有效解决小样本情况下的Hughes效应问题;总体精度达到75%–80%,Kappa系数达到0.61–0.74;比随机子空间集成方法和随机森林方法分类精度更高、更稳定,适合高光谱图像分类。 相似文献
3.
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
4.
目前普遍采用的分类器通常都是针对单一或小量任务而设计的,在小数据量的处理中能取得比较满意的结果。但对于海量遥感数据的处理,其在处理时效和分类精度方面还有待研究。本文以遥感图像场景分类任务为例,着重对遥感数据分类问题中几种典型分类方法的适用性进行比较研究,包括K近邻(KNN)、随机森林(RF),支持向量机(SVM)和稀疏表达分类器(SRC)等。分别从参数敏感性,训练样本数据量,待分类样本数据量和样本特征维数对分类器性能的影响等几个方面进行比较分析。实验结果表明:(1)KNN,RF和L0-SRC方法相比RBF-SVM,Linear-SVM和L1-SRC,受参数影响的程度更弱;(2)待分类样本固定的情况下,随着训练样本数目的增加,SRC类型分类方法的分类性能最佳,SVM类型方法次之,然后是RF和KNN,在总体分类时间上呈现出L0-SRCL1-SRCRFRBF-SVM/Linear-SVMKNN/L0-SRC-Batch的趋势;(3)训练样本固定的情况下,所有分类方法的分类精度几乎都不受待分类样本数目变化的影响,RBF-SVM方法性能最佳,其次是L1-SRC,然后是Linear-SVM,最后是RF和L0-SRC/L0-SRC-Batch,在总体分类时间上,L1-SRC和L0-SRC相比其他分类方法最为耗时;(4)样本特征维数的变化不仅影响分类器的运行效率,同时也影响其分类精度,其中SRC和KNN分类器器无需较高的特征维数即可获得较好的分类结果,SVM对高维特征具有较强的包容性和学习能力,RF分类器对特征维数增加则表现得并不敏感,特征维数的增加并不能对其分类精度的提升带来更多的贡献。总的来说,在大数据量的遥感数据分类任务中,现有分类方法具有良好的适用性,但是对于分类器的选择应当基于各自的特点和优势,结合实际应用的特点进行权衡和选择,选择参数敏感性较小,分类总体时间消耗低但分类精度相对较高的分类方法。 相似文献
5.
Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers. 相似文献
6.
7.
为提高高光谱图像的分类精度,提出了一种融合空谱特征和集成超限学习机的高光谱图像分类方法。首先结合每个像素邻域的光谱信息提取空谱特征向量;考虑到高光谱相邻波段信息具有一定的相关性,先对提取的特征向量进行平均分组,然后从每个区间随机选择若干个波段进行组合,采用具有快速学习能力的超限学习机训练分类器。为提高分类模型的泛化能力,基于集成学习思想,对提取的空谱特征进行多次抽样,训练得到多个弱分类器,最后采用投票表决法得到用于高光谱图像分类的强分类器。采用3个典型高光谱数据进行了分类试验,试验结果表明,提出的算法总体分类精度较优,尤其当训练样本数较少时能取得较高的分类精度。提出的算法具有可调参数少、训练速度快、分类精度高等优点,具有广阔的应用前景。 相似文献
8.
The objective of this paper is to demonstrate a new method to map the distributions of C3 and C4 grasses at 30 m resolution and over a 25-year period of time (1988–2013) by combining the Random Forest (RF) classification algorithm and patch stable areas identified using the spatial pattern analysis software FRAGSTATS. Predictor variables for RF classifications consisted of ten spectral variables, four soil edaphic variables and three topographic variables. We provided a confidence score in terms of obtaining pure land cover at each pixel location by retrieving the classification tree votes. Classification accuracy assessments and predictor variable importance evaluations were conducted based on a repeated stratified sampling approach. Results show that patch stable areas obtained from larger patches are more appropriate to be used as sample data pools to train and validate RF classifiers for historical land cover mapping purposes and it is more reasonable to use patch stable areas as sample pools to map land cover in a year closer to the present rather than years further back in time. The percentage of obtained high confidence prediction pixels across the study area ranges from 71.18% in 1988 to 73.48% in 2013. The repeated stratified sampling approach is necessary in terms of reducing the positive bias in the estimated classification accuracy caused by the possible selections of training and validation pixels from the same patch stable areas. The RF classification algorithm was able to identify the important environmental factors affecting the distributions of C3 and C4 grasses in our study area such as elevation, soil pH, soil organic matter and soil texture. 相似文献
9.
Davood Akbari 《Journal of the Indian Society of Remote Sensing》2017,45(6):927-937
This paper aims to improve the accuracy and the efficiency of high resolution land cover mapping in urban area. To this end, an improved approach for classification of hyperspectral imagery is proposed and evaluated. This approach benefits from both inherent spectral and spatial information of an image. The weighted genetic (WG) algorithm is first used to obtain the subspace of hyperspectral data. The obtained features are then fed into the enhanced marker-based minimum spanning forest (EMSF) classification algorithm. In this algorithm, the markers are extracted from the classification maps obtained by both support vector machine and watershed segmentation algorithm classifiers. For this purpose, the class’s pixels with the largest population in the classification map are kept for each region of the segmentation map. Then, the most reliable classified pixels are chosen from among the exiting pixels as markers. To evaluate the efficiency of the proposed approach, three hyperspectral data sets acquired by ROSIS-03, Hymap and Hyper-Cam LWIR are used. Experimental results showed that the proposed WG–EMSF approach achieves approximately 9, 8 and 6% better overall accuracy than the original MSF-based algorithm for these data sets respectively. 相似文献
10.
高光谱遥感影像多级联森林深度网络分类算法 总被引：1，自引：1，他引：0
高光谱遥感技术在环境监测、应急保障、精细地物提取等方面有着广泛的应用,随着高分五号高光谱数据的正式发布,高光谱遥感技术将发挥更重要的作用。遥感影像分类作为高光谱遥感影像信息处理的重要部分,已成为当前研究重点。本文针对传统多级联森林深度学习中模型复杂、无法利用基分类器差异信息、对类间差异较小的样本无法正确区分等不足,提出了一种改进的多级联森林深度学习模型,在模型框架中,分别采用了随机森林和旋转森林作为基分类器,并引入逻辑回归分类器作为判别器用于训练层扩展。相较于传统的深度神经网络,改进的多级联森林深度网络超参数较少且能够自适应确定训练层,更方便进行模型优化。实验采用了高分五号数据集及两个公开的高光谱数据集(Indian Pines数据集及Pavia University数据集)进行精度评定,同时选择了传统分类器支持向量机、深度置信网等模型作为对比分析。实验结果表明,改进的多级联森林深度学习模型能有效地进行高光谱遥感影像分类,且较传统的分类方法精度有所提升。 相似文献
11.
Many experiments of object-based image analysis have been conducted in remote sensing classification. However, they commonly used high-resolution imagery and rarely focused on suburban area. In this research, with the Landsat-8 imagery, classification of a suburban area via the object-based approach is achieved using four classifiers, including decision tree (DT), support vector machine (SVM), random trees (RT), and naive Bayes (NB). We performed feature selection at different sizes of segmentation scale and evaluated the effects of segmentation and tuning parameters within each classifier on classification accuracy. The results showed that the influence of shape on overall accuracy was greater than that of compactness, and a relatively low value of shape should be set with increasing scale size. For DT, the optimal maximum depth usually varied from 5 to 8. For SVM, the optimal gamma was less than or equal to 10?2, and its optimal C was greater than or equal to 102. For RT, the optimal active variables was less than or equal to 4, and the optimal maximum tree number was greater than or equal to 30. Furthermore, although there was no statistically significant difference between some classification results produced using different classifiers, SVM has a slightly better performance. 相似文献
12.
Mohamed Barakat A. Gibril Suzana A. Bakar Kouame Yao Mohammed Oludare Idrees 《国际地球制图》2017,32(7):735-748
In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images. 相似文献
13.
Classifying historical remotely sensed imagery using a tempo-spatial feature evolution (T-SFE) model 总被引：1，自引：0，他引：1
Yichun Xie Zongyao Sha Yongfei Bai 《ISPRS Journal of Photogrammetry and Remote Sensing》2010,65(2):182-190
Large and growing archives of orbital imagery of the earth’s surface collected over the past 40 years provide an important resource for documenting past and current land cover and environmental changes. However uses of these data are limited by the lack of coincident ground information with which either to establish discrete land cover classes or to assess the accuracy of their identification. Herein is proposed an easy-to-use model, the Tempo-Spatial Feature Evolution (T-SFE) model, designed to improve land cover classification using historical remotely sensed data and ground cover maps obtained at later times. This model intersects (1) a map of spectral classes (S-classes) of an initial time derived from the standard unsupervised ISODATA classifier with (2) a reference map of ground cover types (G-types) of a subsequent time to generate (3) a target map of overlaid patches of S-classes and G-types. This model employs the rules of Count Majority Evaluation, and Subtotal Area Evaluation that are formulated on the basis of spatial feature evolution over time to quantify spatial evolutions between the S-classes and G-types on the target map. This model then applies these quantities to assign G-types to S-classes to classify the historical images. The model is illustrated with the classification of grassland vegetation types for a basin in Inner Mongolia using 1985 Landsat TM data and 2004 vegetation map. The classification accuracy was assessed through two tests: a small set of ground sampling data in 1985, and an extracted vegetation map from the national vegetation cover data (NVCD) over the study area in 1988. Our results show that a 1985 image classification was achieved using this method with an overall accuracy of 80.6%. However, the classification accuracy depends on a proper calibration of several parameters used in the model. 相似文献
14.
Land use/cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land use/cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims at establishing an efficient classification approach to accurately map all broad land use/cover classes in a large, heterogeneous tropical area, as a basis for further studies (e.g., land use/cover change, deforestation and forest degradation). Specifically, we first compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbor and four different support vector machines – SVM), and hybrid (unsupervised–supervised) classifiers, using hard and soft (fuzzy) accuracy assessments. We then assess, using the maximum likelihood algorithm, what textural indices from the gray-level co-occurrence matrix lead to greater classification improvements at the spatial resolution of Landsat imagery (30 m), and rank them accordingly. Finally, we use the textural index that provides the most accurate classification results to evaluate whether its usefulness varies significantly with the classifier used. We classified imagery corresponding to dry and wet seasons and found that SVM classifiers outperformed all the rest. We also found that the use of some textural indices, but particularly homogeneity and entropy, can significantly improve classifications. We focused on the use of the homogeneity index, which has so far been neglected in land use/cover classification efforts, and found that this index along with reflectance bands significantly increased the overall accuracy of all the classifiers, but particularly of SVM. We observed that improvements in producer's and user's accuracies through the inclusion of homogeneity were different depending on land use/cover classes. Early-growth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land use/cover classes were mapped with producer's and user's accuracies of ∼90%. Our classification approach seems very well suited to accurately map land use/cover of heterogeneous landscapes, thus having great potential to contribute to climate change mitigation schemes, conservation initiatives, and the design of management plans and rural development policies. 相似文献
15.
16.
Evaluation of Goddard’s LiDAR,hyperspectral, and thermal data products for mapping urban land-cover types 总被引：1，自引：0，他引：1
Goddard’s LiDAR (Light Detection And Ranging), hyperspectral and thermal (G-LiHT) airborne imager is a new system to advance concepts of data fusion for worldwide applications. A recent G-LiHT mission conducted in June 2016 over an urban area opens a new opportunity to assess the G-LiHT products for urban land-cover mapping. In this study, the G-LiHT hyperspectral and LiDAR-canopy height model (LiDAR-CHM) products were evaluated to map five broad land-cover types. A feature/decision-level fusion strategy was developed to integrate two products. Contemporary data processing techniques were applied, including object-based image analysis, machine-learning algorithms, and ensemble analysis. Evaluation focused on the capability of G-LiHT hyperspectral products compared with multispectral data with similar spatial resolution, the contribution of LiDAR-CHM, and the potential of ensemble analysis in land-cover mapping. The results showed that there was no significant difference between the application of the G-LiHT hyperspectral product and simulated Quickbird data in the classification. A synthesis of G-LiHT hyperspectral and LiDAR-CHM products achieved the best result with an overall accuracy of 96.3% and a Kappa value of 0.95 when ensemble analysis was applied. Ensemble analysis of the three classifiers not only increased the classification accuracy but also generated an uncertainty map to show regions with a robust classification as well as areas where classification errors were most likely to occur. Ensemble analysis is a promising tool for land-cover classification. 相似文献
17.
M. Heinl J. Walde G. Tappeiner U. Tappeiner 《International Journal of Applied Earth Observation and Geoinformation》2009
The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification. 相似文献
18.
19.
高光谱-LiDAR多级融合城区地表覆盖分类 总被引：3，自引：3，他引：0
城市地区地表覆盖分类在城市研究中是一个十分重要的方向。遥感作为获取地物物理属性的一种重要技术手段,已初步应用于分类研究中。然而,随着城镇化的不断推进,城市内部地物类型越来越复杂,单一的遥感影像已无法满足城区地表覆盖分类中高精度的要求。高光谱影像和LiDAR数据能够分别表征地物的光谱信息及高程而被广泛应用。因此,根据两者之间互补的优势,本文提出了基于高光谱影像和LiDAR数据多级融合的城区地表覆盖分类方法。首先对两幅影像分别进行特征提取,将提取到的光谱、空间及高程信息进行层叠实现特征级融合。对得到的特征影像的所有像素点进行分类,然后利用LiDAR点云数据提取的建筑物掩膜,对非建筑物部分进行分类,再次实现特征级融合,以此改善建筑物区域与非建筑物区域的混淆。然后将未使用掩膜得到的分类结果与利用掩膜得到的分类结果进行投票实现决策级融合。最后利用条件随机场模型对分类结果进行后处理操作,达到平滑图像去除噪声点的目的。 相似文献
20.
In the present study, forest type classification using Landsat TM False Colour Composite (FCC) bands 2, 3, 4 has been evaluated for mapping highly heterogeneous forest environment of Western Ghats (Kerala). Visual interpretation of Landsat TM FCC has been carried out to identify bioclimatic vegetation types. For accuracy estimation maps prepared from 1∶15,000 scale black-and-white aerial photographs have been used as ground check data. For comparison aerial photomap classes have been aggregated to match with Landsat-TM-derived map. The classification accuracy of ten major bioclimatic and landcover types was estimated using systematic sampling procedure. The overall classification accuracy of the forest types for the study area was 88.33%. 相似文献