Impervious surface extraction from high-resolution satellite image using pixel- and object-based hybrid analysis

High-resolution satellite imaging provides a wealth of details about the Earth's surface, but it is still a challenge to determine the complex, impervious surface from high-resolution satellite images. A pixel- and object-based hybrid analysis (POHA) method is proposed for the extraction task. Pixel-based analysis is first applied to provide prior knowledge; then, based on prior knowledge, the subsequent object-based analysis is simply to find similar rather than new impervious objects using a weighted minimum distance strategy. In order to combine different image analysis methods, the segmentation masking strategy was introduced to transform the image analysis from pixel level to object level. A QuickBird image of Hangzhou City in China was used to test POHA. Furthermore, POHA was compared with both the pixel-based analysis and object-based image analysis (OBIA) methods, showing that POHA runs with limited human–computer interactions, and can provide accurate impervious surface mapping.     

Requirements for labelling forest polygons in an object-based image analysis classification

The ability to spatially quantify changes in the landscape and create land-cover maps is one of the most powerful uses of remote sensing. Recent advances in object-based image analysis (OBIA) have also improved classification techniques for developing land-cover maps. However, when using an OBIA technique, collecting ground data to label reference units may not be straightforward, since these segments generally contain a variable number of pixels as well as a variety of pixel values, which may reflect variation in land-cover composition. Accurate classification of reference units can be particularly difficult in forested land-cover types, since these classes can be quite variable on the ground. This study evaluates how many prism sample locations are needed to attain an acceptable level of accuracy within forested reference units in southeastern New Hampshire (NH). Typical forest inventory guidelines suggest at least 10 prism samples per stand, depending on the stand area and stand type. However, because OBIA segments group pixels based on the variance of the pixels, fewer prism samples may be necessary in a segment to properly estimate the stand composition. A bootstrapping statistical technique was used to find the necessary number of prism samples to limit the variance associated with estimating the species composition of a segment. Allowing for the lowest acceptable variance, a maximum of only six prism samples was necessary to label forested reference units. All polygons needed at least two prism samples for classification.     

Accurate crop-type classification using multi-temporal optical and multi-polarization SAR data in an object-based image analysis framework

Accurate crop-type classification is a challenging task due, primarily, to the high within-class spectral variations of individual crops during the growing season (phenological development) and, second, to the high between-class spectral similarity of crop types. Utilizing within-season multi-temporal optical and multi-polarization synthetic aperture radar (SAR) data, this study introduces a combined object- and pixel-based image classification methodology for accurate crop-type classification. Particularly, the study investigates the improvement of crop-type classification by using the least number of multi-temporal RapidEye (RE) images and multi-polarization Radarsat-2 (RS-2) data utilized in an object- and pixel-based image analysis framework. The method was tested on a study area in Manitoba, Canada, using three different classifiers including the standard Maximum Likelihood (ML), Decision Tree (DT), and Random Forest (RF) classifiers. Using only two RE images of July and August, the proposed method results in overall accuracies (OAs) of about 95%, 78%, and 93% for the ML, DT, and RF classifiers, respectively. Moreover, the use of only two quad-pol images of RS-2 of June and September resulted in OAs of 92%, 75%, and 90% for the ML, DT, and RF classifiers, respectively. The best classification results were achieved by the synergistic use of two RE and two RS-2 images. In this case, the overall classification accuracies were 97% for both ML and RF classifiers. In addition, the average producer’s accuracies of 95% and 96% were achieved by the ML and RF classifiers, respectively, whereas the average user accuracy was 94% for both classifiers. The results indicated promising potentials for rapid and cost-effective local-scale crop-type classification using a limited number of high-resolution optical and multi-polarization SAR images. Very accurate classification results can be considered as a replacement for sampling the agricultural fields at the local scale. The result of this very accurate classification at discrete locations (approximately 25 × 25 km frames) can be applied in a separate procedure to increase the accuracy of crop area estimation at the regional to provincial scale by linking these local very accurate spatially discrete results to national wall-to-wall continuous crop classification maps.     

Active learning for object-based image classification using predefined training objects

Object-based image analysis (OBIA) is a new remote-sensing-based image processing technology that has become popular in recent years. In spite of its remarkable advantages, the segmentation results that it generates feature a large number of mixed objects owing to the limitations of OBIA segmentation technology. The mixed objects directly influence the acquisition of training samples and the labelling of objects and thus affect the stability of classification performance. In light of this issue, this article evaluates the influence of classification uncertainty on classification performance and proposes a sampling strategy based on active learning. This sampling strategy is novel in two ways: (1) information entropy is used to evaluate the classification uncertainty of segmented objects; all segmented objects are classified as having zero or non-zero entropies, and the latter are arranged in terms of decreasing entropy. (2) Based on an evaluation of the influence of classification uncertainty on classification performance, an active learning technology is developed. A certain proportion of zero-entropy objects is acquired via random sampling used as seed training samples for active learning, non-zero-entropy objects are used as a candidate set for active learning, and the entropy query-by-bagging (EQB) algorithm is used to conduct active learning to acquire optimal training samples. In this study, three groups of high-resolution images were tested. The test results show that zero-entropy and non-zero-entropy objects are indispensable to the classifier, where the optimal range of the ratio of combination of the two is between 0.2 and 0.6. Moreover, the proposed sampling strategy can effectively improve the stability and accuracy of classification.     

Land-cover classification using radar and optical images: a case study in Central Mexico

Land-cover studies based on optical remote sensing in regions which exhibit disorderly urban growth and quick-use conversion of farmland to non-farm usage face problems due to inaccurate discrimination of cover types and hence inaccurate extent estimations. The use of data in the visible and infrared areas of the electromagnetic spectrum for classifying crop types has been extensively explored, concluding that data acquisitions must be made during critical crop development periods. This raises a concern in Central Mexico where such periods coincide with important cloud coverage and where good estimates of the extent of agricultural areas and of particular crops are keenly sought by government agencies for planning purposes. Due to the interest in accurate and updated maps for this area, repeated studies have been carried out over a number of years by the National Institute of Research for Forestry, Agriculture and Livestock for the Ministry of Agriculture of Mexico. Taking into consideration the difficulties of acquiring and analysing data derived from optical sensors, the objective of this study was to assess the advantages of combining synthetic aperture radar (SAR) and optical remote sensing in producing more accurate maps. The study area covers 15 634 ha and is located in Central Mexico in a region where agricultural plots of varied sizes and forms are interspersed with rapid urbanization spaces. We investigated alternative supervised classification schemes combining the Radarsat-1 C-band with Landsat Enhanced Thematic Mapper Plus (ETM+) bands to estimate land cover distributions and assess the quality of results with field data. Then, we set forth and evaluated a methodology which applies data fusion of selected Landsat ETM+?bands and the C-radar band. The separation and similarities for vegetated and non-vegetated cover types depends on whether the selected agricultural crops are annual or perennial, and on whether there are bare soils present. This knowledge for the particular study area influenced the selection of dates for image take and analysis. Partial fused and non-fused land-cover maps were assessed for accuracy and were combined to obtain a final map. The results demonstrate that the combined utilization of optical and radar imagery yields useful land cover information and improved classification accuracy over those obtained using either type of image on its own.     

面向对象的高分辨率SAR图像处理及应用

目的随着合成孔径雷达(SAR)技术和分辨率的不断提高,越来越多的空间细节呈现在高分辨率SAR影像上。与此同时,SAR图像的数据量越来越大,人们对其应用需求也越来越高,这使得传统的基于像素的SAR处理方法不再适用。面向对象分析技术以像元集合——"对象"为分析单元,为高分辨率遥感图像处理提供了有效的思路,并日渐成为遥感、摄影测量以及GIS等领域所关注的对象和研究热点之一。目前该技术在光学遥感中已经得到了广泛的应用,但在SAR图像处理中的应用还处于起步阶段。方法本文在简要阐述面向对象分析技术起源和特点的基础上,对SAR图像面向对象技术中常用的多尺度分割算法进行了分类分析,接着对面向对象技术在SAR遥感的应用方向进行全面介绍,最后对面向对象技术在SAR上的应用进行了总结与展望。结果面向对象分析技术在SAR图像处理中的应用主要分为以下五个方面:地物分类、城市信息提取、变化检测、海洋应用、森林应用。结论面向对象分析技术在解决高分辨率SAR图像尺度效应、抑制噪声等方面有着重要作用。目前,国外学者在基于SAR的面向对象分析技术研究上已经取得了一定的进展,但总体上该技术仍面临诸多问题,需要进一步的研究和完善。     

Accuracy assessment of object-based image classification: another STEP

This article proposes STEP, a novel object-based similarity matrix, for assessing both geometric and thematic accuracies of remote-sensing image classification. In contrast to the traditional error matrix, STEP uses samples of classified and reference objects rather than counts of pixels. Moreover, STEP provides four (4) similarity metrics for characterization of classified objects compared with reference objects: (i) shape similarity (S); (ii) theme similarity (T); (iii) edge similarity (E); and (iv) position similarity (P). Individual objects’ similarity metrics are grouped by thematic class and expressed in the integrated STEP similarity matrix. The proposed approach is illustrated using both a hypothetical classification and a real urban land-cover classification obtained from high spatial resolution orthoimagery. Results show that the STEP indices and matrices are able to express meaningful information about thematic and geometric accuracies of object-based image classifications. It also yields area weighted aggregated-by-class error matrices that allow for calculating overall accuracy metrics.     

Mapping vegetation in a late Quaternary landform of the Amazonian wetlands using object-based image analysis and decision tree classification

Fan-shaped morphologies related to late Quaternary residual megafan depositional systems are common features over wide areas in northern Amazonia. These features were formed by ancient distributary drainage systems that are in great contrast to tributary drainage networks that typify the modern Amazon basin. The surfaces of the Amazonian megafans constitute vegetacional mosaic wetlands with different campinarana types. A fine-scale-resolution investigation is required to provide detailed classification maps for the various campinarana and surrounding forest types associated with the Amazonian megafans. This approach remains to be presented, despite its relevance for analysing the relationship between stages of plant succession and sedimentary dynamics associated with the evolution of megafans. In this work, we develop a methodology for classifying vegetation over a fan-shaped megafan palaeoform from a northern Amazonian wetland. The approach included object-based image analysis (OBIA) and data-mining (DM) techniques combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, land-cover fractions derived by the linear spectral mixing model, synthetic aperture radar (SAR) images, and the digital elevation model (DEM) acquired during the Shuttle Radar Topography Mission (SRTM). The DEM, vegetation fraction, and ASTER band 3 were the most useful parameters for defining the forest classes. The normalized difference vegetation index (NDVI), ASTER band 1, vegetation fraction, and the Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) transmitting and receiving horizontal polarization (HH) and transmitting horizontal and receiving vertical polarization (HV) were all effective in distinguishing the wetland classes campinarana and Mauritia. Tests of statistical significance indicated the overall accuracies and kappa coefficients (κ) of 88% and 0.86 for the final map, respectively. The allocation disagreement coefficient of 5% and a quantity disagreement value of 7% further attested the statistical significance of the classification results. Hence, in addition to water, exposed soil, and deforestation areas, OBIA and DM were successful for differentiating a large number of open (forest, wood, shrub, and grass campinaranas), forest (terra firme, várzea, igapó, and alluvial), as well as Mauritia wetland classes in the inner and outer areas of the studied megafan.     

Object-based feature selection using class-pair separability for high-resolution image classification

ABSTRACT

With the increasing popularity of object-based image analysis (OBIA) since 2006, numerous classification and mapping tasks were reported to benefit from this evolving paradigm. In these studies, segments are firstly created, followed by classification based on segment-level information. However, the feature space formed by segment-level feature variables can be very large and complex, posing challenges to obtaining satisfactory classification performance. Accordingly, this work attempts to develop a new feature selection approach for segment-level features. Based on the principle of class-pair separability, the segment-level features are grouped according to their types. For each group, the contribution of each segment-level feature to the separation of a pair of classes is quantified. With the information of all feature groups and class pairs, the separability ranking and appearance frequency are considered to compute importance score for each feature. Higher importance score means larger appropriateness to select a feature. By using two Gaofen-2 multi-spectral images, the proposed method is validated. The experimental results show the advantages of the proposed technique over some state-of-the-art feature selection approaches: (1) it can better reduce the number of segment-level features and effectively avoid redundant information; (2) the feature subset obtained by the proposed scheme has good potential to improve classification accuracy.     

Classification of impervious land-use features using object-based image analysis and data fusion

The proportion of impervious area within a watershed is a key indicator of the impacts of urbanization on water quality and stream health. Research has shown that object-based image analysis (OBIA) techniques are more effective for urban land-cover classification than pixel-based classifiers and are better suited to the increased complexity of high-resolution imagery. Focusing on five 2-km² study areas within the Black Creek sub-watershed of the Humber River, this research uses eCognition® software to develop a rule-based OBIA workflow for semi-automatic classification of impervious land-use features (e.g., roads, buildings, Parking Lots, driveways). The overall classification accuracy ranges from 88.7 to 94.3%, indicating the effectiveness of using an OBIA approach and developing a sequential system for data fusion and automated impervious feature extraction. Similar accuracy results between the calibrating and validating sites demonstrates the strong potential for the transferability of the rule-set from pilot study sites to a larger area.     

New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images

The use of asbestos cement (AC) roofing materials is a significant concern because of their deleterious effects on human health and the environment. The main objective of this study was to map AC roofs from WorldView-2 (WV-2) images using object-based image analysis (OBIA). A robust Taguchi optimization technique was used to optimize segmentation parameters for WV-2 images in heterogeneous urban areas. In this research, two subsets of WV-2 satellite image sets were utilized to map AC roofs. Rule-based OBIA framework was developed on the first study area. Different supervised OBIA classifiers, such as Bayes, k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF), were tested on the first image of the study areas to evaluate the performance of a rule-based classifier. Results of the supervised classifiers showed confusion between AC roof class and some urban features, with overall accuracies of 72.21%, 77%, 81.75%, and 82.02% for Bayes, k-NN, SVM, and RF, respectively. To assess the transferability of the proposed method, the adopted classification framework was applied to larger subsets of WV-2 of the second study area. The results of the proposed approach showed outstanding performance, with overall accuracies of 93.10% and 90.74% for the first and second classified images, respectively. The McNemar test emphasized the statistical reliability of rule-based result (in the first site) compared with supervised classification results. Therefore, the proposed framework of using rule-based classification and Taguchi optimization technique provide an efficient and expeditious approach to mapping and monitoring the presence of AC roofs and help local authorities in their decision-making strategies and policies.     

Land-cover classification of partly missing data using support vector machines

Land-cover classification based on multi-temporal satellite images for scenarios where parts of the data are missing due to, for example, clouds, snow or sensor failure has received little attention in the remote-sensing literature. The goal of this article is to introduce support vector machine (SVM) methods capable of handling missing data in land-cover classification. The novelty of this article consists of combining the powerful SVM regularization framework with a recent statistical theory of missing data, resulting in a new method where an SVM is trained for each missing data pattern, and a given incomplete test vector is classified by selecting the corresponding SVM model. The SVM classifiers are evaluated on Landsat Enhanced Thematic Mapper Plus (ETM?+?) images covering a scene of Norwegian mountain vegetation. The results show that the proposed SVM-based classifier improves the classification accuracy by 5–10% compared with single image classification. The proposed SVM classifier also outperforms recent non-parametric k-nearest neighbours (k-NN) and Parzen window density-based classifiers for incomplete data by about 3%. Moreover, since the resulting SVM classifier may easily be implemented using existing SVM libraries, we consider the new method to be an attractive choice for classification of incomplete data in remote sensing.     

Land-cover classification using multi-temporal GF-1 wide field view data

ABSTRACT

Vegetation is an important land-cover type and its growth characteristics have potential for improving land-cover classification accuracy using remote-sensing data. However, due to lack of suitable remote-sensing data, temporal features are difficult to acquire for high spatial resolution land-cover classification. Several studies have extracted temporal features by fusing time-series Moderate Resolution Imaging Spectroradiometer data and Landsat data. Nevertheless, this method needs assumption of no land-cover change occurring during the period of blended data and the fusion results also present certain errors influencing temporal features extraction. Therefore, time-series high spatial resolution data from a single sensor are ideal for land-cover classification using temporal features. The Chinese GF-1 satellite wide field view (WFV) sensor has realized the ability of acquiring multispectral data with decametric spatial resolution, high temporal resolution and wide coverage, which contain abundant temporal information for improving land-cover classification accuracy. Therefore, it is of important significance to investigate the performance of GF-1 WFV data on land-cover classification. Time-series GF-1 WFV data covering the vegetation growth period were collected and temporal features reflecting the dynamic change characteristics of ground-objects were extracted. Then, Support Vector Machine classifier was used to land-cover classification based on the spectral features and their combination with temporal features. The validation results indicated that temporal features could effectively reflect the growth characteristics of different vegetation and finally improved classification accuracy of approximately 7%, reaching 92.89% with vegetation type identification accuracy greatly improved. The study confirmed that GF-1 WFV data had good performances on land-cover classification, which could provide reliable high spatial resolution land-cover data for related applications.     

Study of land cover classification based on knowledge rules using high-resolution remote sensing images

This paper deals with the limitations of visual interpretation of high-resolution remote sensing images and of automatic computer classification completely dependent on spectral data. A knowledge-rule method is proposed, based on spectral features, texture features obtained from the gray-level co-occurrence matrix, and shape features. QuickBird remote sensing data were used for an experimental study of land-use classification in the combination zone between urban and suburban areas in Beijing. The results show that the deficiencies of methods where only spectral data are used for classification can be eliminated, the problem of similar spectra in multispectral images can be effectively solved for the classification of ground objects, and relatively high classification accuracy can be reached.     

Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data

Recent satellite missions have provided new perspectives by offering high spatial resolution, a variety of spectral properties, and fast revisit rates to the same regions. In this study, we examined the utility of both broadband red-edge spectral information and texture features for classifying paddy rice crops in South Korea into three different growth stages. The rice grown in South Korea can be grouped into early-maturing, medium-maturing, and medium-late-maturing cultivars, and each cultivar is known to have a minimum and maximum productivity. Therefore, the accurate classification of paddy rice crops into a certain time line enables pre-estimation of the expected rice yields. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the paddy rice crops, particularly when single-season image data were used. In contrast, texture information resulted in only minor improvement or even a slight decline in accuracy, although it is known to be advantageous for object-based classification. This was due to the homogeneous nature of paddy rice fields, as different rice cultivars are similar in terms of their morphology. Based on these results, we conclude that the additional spectral information such as the red-edge band is more useful than the texture features to detect different crop conditions in relatively homogeneous rice paddy environments. We therefore confirm the potential of broadband red-edge information to improve the classification of paddy rice crops. However, there is still a need to examine the relationship between textural properties and paddy rice crop parameters in greater depth.     

The development of an operational procedure for burned-area mapping using object-based classification and ASTER imagery

Although burned-area mapping at a regional level is traditionally based on the use of Landsat data, the potential gap in the sensor's data collection emphasizes the need to find alternative data sources to be used in the operational mapping of burned areas. This work aims to investigate whether it is possible to develop a transferable object-based classification model for burned-area mapping using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. The initial step in the investigation involved the development of an object-based classification model for accurately mapping burned areas in central Portugal using an ASTER image, and subsequently an examination of its performance when mapping a burned area located on the island of Rhodes, Greece, using a different ASTER image. Results indicate that the combined use of object-based image analysis and ASTER imagery can provide an alternative operational tool that could be used to identify and map burned areas and thus fill a potential gap in Landsat data collection.     

Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests

The random forest (RF) classifier is a relatively new machine learning algorithm that can handle data sets with large numbers and types of variables. Multi-scale object-based image analysis (MOBIA) can generate dozens, and sometimes hundreds, of variables used to classify earth observation (EO) imagery. In this study, a MOBIA approach is used to classify the land cover in an area undergoing intensive agricultural development. The information derived from the elevation data and imagery from two EO satellites are classified using the RF algorithm. Using a wrapper feature selection algorithm based on the RF, a large initial data set consisting of 418 variables was reduced by ～60%, with relatively little loss in the overall classification accuracy. With this feature-reduced data set, the RF classifier produced a useable depiction of the land cover in the selected study area and achieved an overall classification accuracy of greater than 90%. Variable importance measures produced by the RF algorithm provided an insight into which object features were relatively more important for classifying the individual land-cover types. The MOBIA approach outlined in this study achieved the following: (i) consistently high overall classification accuracies (>85%) using the RF algorithm in all models examined, both before and after feature reduction; (ii) feature selection of a large data set with little expense to the overall classification accuracy; and (iii) increased interpretability of classification models due to the feature selection process and the use of variable importance scores generated by the RF algorithm.     

Wavelet-based image registration technique for high-resolution remote sensing images

Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints at different times or by different sensors. It is an important image processing procedure in remote sensing and has been studied by remote sensing image processing professionals for several decades. Nevertheless, it is still difficult to find an accurate, robust, and automatic image registration method, and most existing image registration methods are designed for a particular application. High-resolution remote sensing images have made it more convenient for professionals to study the Earth; however, they also create new challenges when traditional processing methods are used. In terms of image registration, a number of problems exist in the registration of high-resolution images: (1) the increased relief displacements, introduced by increasing the spatial resolution and lowering the altitude of the sensors, cause obvious geometric distortion in local areas where elevation variation exists; (2) precisely locating control points in high-resolution images is not as simple as in moderate-resolution images; (3) a large number of control points are required for a precise registration, which is a tedious and time-consuming process; and (4) high data volume often affects the processing speed in the image registration. Thus, the demand for an image registration approach that can reduce the above problems is growing. This study proposes a new image registration technique, which is based on the combination of feature-based matching (FBM) and area-based matching (ABM). A wavelet-based feature extraction technique and a normalized cross-correlation matching and relaxation-based image matching techniques are employed in this new method. Two pairs of data sets, one pair of IKONOS panchromatic images from different times and the other pair of images consisting of an IKONOS panchromatic image and a QuickBird multispectral image, are used to evaluate the proposed image registration algorithm. The experimental results show that the proposed algorithm can select sufficient control points semi-automatically to reduce the local distortions caused by local height variation, resulting in improved image registration results.     

OCRS: an interactive object-based image clustering and retrieval system

In this paper, we propose an Interactive Object-based Image Clustering and Retrieval System (OCRS). The system incorporates two major modules: Preprocessing and Object-based Image Retrieval. In preprocessing, an unsupervised segmentation method called WavSeg is used to segment images into meaningful semantic regions (image objects). This is an area where a huge number of image regions are involved. Therefore, we propose a Genetic Algorithm based algorithm to cluster these images objects and thus reduce the search space for object-based image retrieval. In the learning and retrieval module, the Diverse Density algorithm is adopted to analyze the user’s interest and generate the initial hypothesis which provides a prototype for future learning and retrieval. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. In interacting with user, we propose to use One-Class Support Vector Machine (SVM) to learn the user’s interest and refine the returned result. Performance is evaluated on a large image database and the effectiveness of our retrieval algorithm is demonstrated through comparative studies.

Land-cover classification of China: Integrated analysis of AVHRR imagery and geophysical data

Over last two decades, numerous studies have used remotely sensed data from the Advanced Very High Resolution Radiometer (AVHRR) sensors to map land use and land cover at large spatial scales, but achieved only limited success. In this paper, we employed an approach that combines both AVHRR images and geophysical datasets (e.g. climate, elevation). Three geophysical datasets are used in this study: annual mean temperature, annual precipitation, and elevation. We first divide China into nine bio-climatic regions, using the long-term mean climate data. For each of nine regions, the three geophysical data layers are stacked together with AVHRR data and AVHRR-derived vegetation index (Normalized Difference Vegetation Index) data, and the resultant multi-source datasets were then analysed to generate land-cover maps for individual regions, using supervised classification algorithms. The nine land-cover maps for individual regions were assembled together for China. The existing land-cover dataset derived from Landsat Thematic Mapper (TM) images was used to assess the accuracy of the classification that is based on AVHRR and geophysical data. Accuracy of individual regions varies from 73% to 89%, with an overall accuracy of 81% for China. The results showed that the methodology used in this study is, in general, feasible for large-scale land-cover mapping in China.