共查询到20条相似文献，搜索用时 62 毫秒
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The updating of classification maps, as new image acquisitions are obtained, raises the problem of ground-truth information (training samples) updating. In this context, semisupervised multitemporal classification represents an interesting though still not well consolidated approach to tackle this issue. In this letter, we propose a novel methodological solution based on this approach. Its underlying idea is to update the ground-truth information through an automatic estimation process, which exploits archived ground-truth information as well as basic indications from the user about allowed/forbidden class transitions from an acquisition date to another. This updating problem is formulated by means of the support vector machine classification approach and a constrained multiobjective optimization genetic algorithm. Experimental results on a multitemporal data set consisting of two multisensor (Landsat-5 Thematic Mapper and European Remote Sensing satellite synthetic aperture radar) images are reported and discussed. 相似文献
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Rhonda D. Phillips Layne T. Watson Randolph H. Wynne Christine E. Blinn 《ISPRS Journal of Photogrammetry and Remote Sensing》2009,64(1):107-116
Feature reduction in a remote sensing dataset is often desirable to decrease the processing time required to perform a classification and improve overall classification accuracy. This paper introduces a feature reduction method based on the singular value decomposition (SVD). This SVD-based feature reduction method reduces the storage and processing requirements of the SVD by utilizing a training dataset. This feature reduction technique was applied to training data from two multitemporal datasets of Landsat TM/ETM+ imagery acquired over a forested area in Virginia, USA and Rondônia, Brazil. Subsequent parallel iterative guided spectral class rejection (pIGSCR) forest/non-forest classifications were performed to determine the quality of the feature reduction. The classifications of the Virginia data were five times faster using SVD-based feature reduction without affecting the classification accuracy. Feature reduction using the SVD was also compared to feature reduction using principal components analysis (PCA). The highest average accuracies for the Virginia dataset (88.34%) and for the Rondônia dataset (93.31%) were achieved using the SVD. The results presented here indicate that SVD-based feature reduction can produce statistically significantly better classifications than PCA. 相似文献
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针对高分辨率遥感影像分类中由于细节特征突出、同质区域光谱测度变异性增大所带来的像素类属的不确定性及模型的不确定性等造成的误分结果,提出一种基于模糊隶属函数的监督分类方法。对同质区域定义高斯隶属函数模型用来表征像素类属不确定性;模糊化该隶属函数参数建立影像模糊隶属函数,以建模同质区域光谱测度的不确定性;用训练样本在所有类别中的模糊隶属函数及原隶属函数（高斯隶属函数）中的隶属度为输入,建立模糊线性神经网络模型作为目标函数,实现分类决策。该算法和经典算法对World View-2全色合成影像及真实影像进行定性和定量分类实验,分类结果验证了文中方法具有更高的分类精度。 相似文献
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Akshara Preethy Byju Anil Kumar Alfred Stein A. Senthil Kumar 《Journal of the Indian Society of Remote Sensing》2018,46(9):1519-1526
This article presents the use of kernel functions in fuzzy classifiers for an efficient land use/land cover mapping. It focuses on handling mixed pixels obtained from a remote sensing image by considering non-linearity between class boundaries. It uses kernel functions combined with the conventional fuzzy c-means (FCM) classifier. Kernel-based fuzzy c-mean classifiers were applied to classify AWiFS and LISS-III images from Resourcesat-1 and Resourcesat-2 satellites. Optimal kernels were obtained from eight single kernel functions. Fractional images generated from high resolution LISS-IV image were used as reference data. Classification accuracy of the FCM classifier increased with 12.93%. Improvement in overall accuracy shows that non-linearity in the dataset was handled adequately. The inverse multiquadratic kernel and the Gaussian kernel with the Euclidean norm were identified as optimal kernels. The study showed that overall classification accuracy of the FCM classifier improved if kernel functions were included. 相似文献
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利用主成分分析改善土地利用变化的遥感监测精度——以珠江三角洲城市用地扩张为例 总被引：41，自引：7，他引：34
近年来,珠江三角洲由于经济的快速发展,城市用地急剧增加,利用多时相的遥感图,可以定量地监测这种城市化的现象。但理,由一般的遥感动态监测方法所得的结果往往夸大变化的程度,以及获得一些不合理的结论．该文提出主成分分析的方法来改善遥感动态监测的精度。将该方法应用应用于珠江三角洲发展最快的东莞市,获得了较满意的结果。 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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Duccio Rocchini 《The Photogrammetric Record》2004,19(106):138-148
Remote sensing represents a powerful method to study the change of several phenomena over time. However, useful input data (for example, aerial photos) can produce misleading information if an inadequate geometric correction is applied. Among mathematical models used for this type of correction, orthorectification (differential correction) seems to be the only one that guarantees accurate results. However, many authors are still basing their results on incorrectly transformed images. This phenomenon is especially due to the incidence of several user-friendly tools and interfaces for the rectification of images. This paper tests both polynomial rectification and orthorectification. Aerial photography of 1954 of the same area was acquired and polynomial rectification and orthorectification were applied, using a 1998 orthophoto as a base. An unsupervised classification was performed by utilising the nearest neighbour method for the resampling of images. The area occupied by each class was calculated. A multitemporal analysis was then performed, considering the overlap between the 1954 orthophoto and the 1998 orthophoto as the base of reference. An overestimate up to 21% of class area was found. When multitemporal analysis was carried out, an overestimate up to 100% of class area change was found. 相似文献
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基于Freeman_Durden分解的全极化SAR影像分类方法能够较好地保持地物极化散射特性,但在分类的过程中,不能改变初始散射机制,导致分解结果对分类精度影响很大。在Freeman_Durden分解中,排列方向相对雷达飞行方向不平行的建筑物（简称为倾斜建筑物）常被分为体散射类型,使得该类建筑物往往被误分为植被。本文通过分析建筑物在SAR影像中的后向散射特性,利用建筑物具有较高相干性的特点,引入最优极化相干系数,在目标分解的基础上通过阈值分割将两者区分开来,进而提高反射非对称性人工目标的分类效果。通过使用E-SAR系统在德国DLR附近Oberpfaffenhofen地区获取的L波段PolInSAR影像和国内X-SAR系统在海南陵水地区获取的X波段PolInSAR影像进行实验,证明本文提出的方法能够有效地将与雷达飞行方向不平行的建筑物与森林区分开。 相似文献
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In the past researchers have suggested hard classification approaches for pure pixel remote sensing data and to handle mixed
pixels soft classification approaches have been studied for land cover mapping. In this research work, while selecting fuzzy
c-means (FCM) as a base soft classifier entropy parameter has been added. For this research work Resourcesat-1 (IRS-P6) datasets
from AWIFS, LISSIII and LISS-IV sensors of same date have been used. AWIFS and LISS-III datasets have been used for classification
and LISS-III and LISS-IV data were used for reference data generation, respectively. Soft classified outputs from entropy
based FCM classifiers for AWIFS and LISS-III datasets have been evaluated using sub-pixel confusion uncertainty matrix (SCM).
It has been observed that output from FCM classifier has higher classification accuracy with higher uncertainty but entropy-based
classifier with optimum value of regularizing parameter generates classified output with minimum uncertainty. 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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Very high spatial and temporal resolution remote sensing data facilitate mapping highly complex and diverse urban environments. This study analyzed and demonstrated the usefulness of combined high-resolution aerial digital images and elevation data, and its processing using object-based image analysis for mapping urban land covers and quantifying buildings. It is observed that mapping heterogeneous features across large urban areas is time consuming and challenging. This study presents and demonstrates an approach for formulating an optimal land cover classification rule set over small representative training urban area image, and its subsequent transfer to the multisensor, multitemporal images. The classification results over the training area showed an overall accuracy of 96%, and the application of rule set to different sensor images of other test areas resulted in reduced accuracies of 91% for the same sensor, 90% and 86% for the different sensors temporal data. The comparison of reference and classified buildings showed ±4% detection errors. Classification through a transferred rule set reduced the classification accuracy by about 5%–10%. However, the trade-off for this accuracy drop was about a 75% reduction in processing time for performing classification in the training area. The factors influencing the classification accuracies were mainly the shadow and temporal changes in the class characteristics. 相似文献
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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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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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Land use classification requires a significant amount of labeled data, which may be difficult and time consuming to obtain. On the other hand, without a sufficient number of training samples, conventional classifiers are unable to produce satisfactory classification results. This paper aims to overcome this issue by proposing a new model, TrCbrBoost, which uses old domain data to successfully train a classifier for mapping the land use types of target domain when new labeled data are unavailable. TrCbrBoost adopts a fuzzy CBR (Case Based Reasoning) model to estimate the land use probabilities for the target (new) domain, which are subsequently used to estimate the classifier performance. Source (old) domain samples are used to train the classifiers of a revised TrAdaBoost algorithm in which the weight of each sample is adjusted according to the classifier’s performance. This method is tested using time-series SPOT images for land use classification. Our experimental results indicate that TrCbrBoost is more effective than traditional classification models, provided that sufficient amount of old domain data is available. Under these conditions, the proposed method is 9.19% more accurate. 相似文献
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S. Ghosh S. Nandy S. Patra S. P. S. Kushwaha A. Senthil Kumar V. K. Dadhwal 《Journal of the Indian Society of Remote Sensing》2017,45(2):327-335
In the present study, parameters derived from Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (GLAS) full waveform were used for land cover classification in western part of Doon valley, Uttarakhand, India. Three parameters, viz, height, front slope angle (afslope) and canopy return ratio (rCanopy) were extracted from the returned full waveform signals. k-means (KM), partitioning around medoids (PAM), and fuzzy c-means (FCM) with different cluster sizes were used for classifying the land cover types with the help of GLAS-derived parameters. Among the clustering methods, KM performed the best. The overall accuracy (89.41 %) of all methods were quite significant with cluster size three i.e. with three classes forest, mango orchard and other class including agriculture, barren/fallow land, settlement, dry river bed, etc. The accuracy of the PAM (60 %) and the FCM (68.4 %) decreased drastically at four clusters with the separation of agriculture from barren/fallow land. The accuracy of the PAM and the FCM further decreased with increase in the number of clusters whereas KM showed reliable results for all clusters. KM with five clusters was able to distinguish five different land covers, viz, forest, mango orchard, agriculture and barren/fallow land and other class including settlement, dry river bed, etc. with an overall classification accuracy of 72.93 %. The study presents a method for classifying land cover types using GLAS full waveform data. 相似文献
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A fuzzy topology-based maximum likelihood classification 总被引：2，自引：0，他引：2
Classification is one of the most widely used remote sensing analysis techniques, with the maximum likelihood classification (MLC) method being a major tool for classifying pixels from an image. Fuzzy topology, in which the set concept is generalized from two values, {0, 1}, to the values of a continuous interval, [0, 1], is a generalization of ordinary topology and is used to solve many GIS problems, such as spatial information management and analysis. Fuzzy topology is induced by traditional thresholding and as such gives a decomposition of MLC classes.Presented in this paper is an image classification modification, by which induced threshold fuzzy topology is integrated into the MLC method (FTMLC). Hence, by using the induced threshold fuzzy topology, each image class in spectral space can be decomposed into three parts: an interior, a boundary and an exterior. The connection theory in induced fuzzy topology enables the boundary to be combined with the interior. That is, a new classification method is derived by integrating the induced fuzzy topology and the MLC method. As a result, fuzzy boundary pixels, which contain many misclassified and over-classified pixels, are able to be re-classified, providing improved classification accuracy. This classification is a significantly improved pixel classification method, and hence provides improved classification accuracy. 相似文献
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Abstract The goal of this research was to explore the utility of very high spatial resolution, digital remotely sensed imagery for monitoring land‐cover changes in habitat preserves within southern California coastal shrublands. Changes were assessed for Los Penasquitos Canyon Preserve, a large open space in San Diego County, over the 1996 to 1999 period for which imagery was available. Multispectral, digital camera imagery from two summer dates, three years apart, was acquired using the Airborne Data Acquisition and Registration (ADAR) digital‐camera system. These very high resolution (VHR) image data (1m), composed of three visible and one near‐infrared wavebands (V/NIR), were the primary image input for assessing land cover change. Image‐derived datasets generated from georeferenced and registered ADAR imagery included multitemporal overlays and multitemporal band differencing with threshold selection. Two different multitemporal image classifications were generated from these datasets and compared. Single‐date imagery was analyzed interactively with image‐derived datasets and with information from field observations in an effort to discern change types. A ground sampling survey conducted soon after the 1999 image acquisition provided concurrent ground reference data. Most changes occurring within the three‐year interval were associated with transitional phenological states and differential precipitation effects on herbaceous cover. Variations in air temperatures and timing of rainfall contributed to differences that the seven‐week image acquisition offset may have caused. Disturbance factors of mechanical clearing, erosion, potentially invasive plants, and fire were evident and their influence on the presence, absence, and type of vegetation cover were likely sources of change signals. The multitemporal VHR, V/NIR image data enabled relatively fine‐scale land cover changes to be detected and identified. Band differencing followed by multitemporal classification provided an effective means for detecting vegetation increase or decrease. Detailed information on short‐term disturbance effects and long‐term vegetation type conversions can be extracted if image acquisitions are carefully planned and geometric and radiometric processing steps are implemented. 相似文献