Land cover classification with coarse spatial resolution data to derive continuous and discrete maps for complex regions

Regularly updated land cover information at continental or national scales is a requirement for various land management applications as well as biogeochemical and climate modeling exercises. However, monitoring or updating of map products with sufficient spatial detail is currently not widely practiced due to inadequate time-series coverage for most regions of the Earth. Classifications of coarser spatial resolution data can be automatically generated on an annual or finer time scale. However, discrete land cover classifications of such data cannot sufficiently quantify land surface heterogeneity or change. This study presents a methodology for continuous and discrete land cover mapping using moderate spatial resolution time series data sets. The method automatically selects sample data from higher spatial resolution maps and generates multiple decision trees. The leaves of decision trees are interpreted considering the sample distribution of all classes yielding class membership maps, which can be used as estimates for the diversity of classes in a coarse resolution cell. Results are demonstrated for the heterogeneous, small-patch landscape of Germany and the bio-climatically varying landscape of South Africa. Results have overall classification accuracies of 80%. A sensitivity analysis of individual modules of the classification process indicates the importance of appropriately chosen features, sample data balanced among classes, and an appropriate method to combine individual classifications. The comparison of classification results over several years not only indicates the method's consistency, but also its potential to detect land cover changes.     

NDVI-derived land cover classifications at a global scale

Phenological differences among vegetation types, reflected in temporal variations in the Normalized Difference Vegetation Index (NDVI) derived from satellite data, have been used to classify land cover at continental scales. Extending this technique to global scales raises several issues: identifying land cover types that are spectrally distinct and applicable at the global scale; accounting for phasing of seasons in different parts of the world; validating results in the absence of reliable information on global land cover; and acquiring high quality global data sets of satellite sensor data for input to land cover classifications. For this study, a coarse spatial resolution (one by one degree) data set of monthly NDVI values for 1987 was used to explore these methodological issues. A result of a supervised, maximum likelihood classification of eleven cover types is presented to illustrate the feasibility of using satellite sensor data to increase the accuracy of global land cover information, although the result has not been validated systematically. Satellite sensor data at finer spatial resolutions that include other bands in addition to NDVI, as well as methodologies to better identify and describe gradients between cover types, could increase the accuracy of results of global land cover data sets derived from satellite sensor data.     

Detection of partial land cover change associated with the migration of inter-class transitional zones

Post-classification comparison techniques are frequently used in studies of change detection. If the classifications used were derived with the use of conventional 'hard' classification techniques, change detection is constrained to the identification of complete changes in class label. This may be inappropriate in circumstances when the land cover conversion is operating at a scale finer than the spatial resolution of the sensor which acquired the imagery and for the detection of land cover modifications. By basing the change detection on the comparison of fuzzy classifications it should be possible to identify partial changes, including both land cover conversion and modification. Fuzzy classifications of Advanced Very High Resolution Radiometer (AVHRR) data were used to identify changes in the apparent position of the forest-savanna transition in West Africa. A comparison of the classifications revealed the variations in the nature and magnitude of land cover change. It was apparent that the migration of the transitional area could be characterized and showed similarity to a simple hypothesized model of land cover change. The comparison of fuzzy classifications was able to provide a richer information base on class membership and its dynamics than that obtainable through the comparison of conventional 'hard' classifications.     

Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the ‘hard’ classifications. Moreover, the outputs derived from the artificial neural network and the fuzzy c-means algorithm in particular were strongly related to the land cover on the ground and provided the most accurate land cover representations. The ability to appropriately represent fuzzy land cover and evaluate the accuracy of the representation should facilitate the use of remote sensing as a source of land cover data.     

The effects of aggregated land cover data on estimating NPP in northern Wisconsin

Ecosystem models are routinely used to estimate net primary production (NPP) from the stand to global scales. Complex ecosystem models, implemented at small scales (< 10 km²), are impractical at global scales and, therefore, require simplifying logic based on key ecological first principles and model drivers derived from remotely sensed data. There is a need for an improved understanding of the factors that influence the variability of NPP model estimates at different scales so we can improve the accuracy of NPP estimates at the global scale. The objective of this study was to examine the effects of using leaf area index (LAI) and three different aggregated land cover classification products-two factors derived from remotely sensed data and strongly affect NPP estimates-in a light use efficiency (LUE) model to estimate NPP in a heterogeneous temperate forest landscape in northern Wisconsin, USA. Three separate land cover classifications were derived from three different remote sensors with spatial resolutions of 15, 30, and 1000 m. Average modeled net primary production (NPP) ranged from 402 gC m^− 2 year^− 1 (15 m data) to 431 gC m^− 2 year^− 1 (1000 m data), for a maximum difference of 7%. Almost 50% of the difference was attributed each to LAI estimates and land cover classifications between the fine and coarse scale NPP estimate. Results from this study suggest that ecosystem models that use biome-level land cover classifications with associated LUE coefficients may be used to model NPP in heterogeneous land cover areas dominated by cover types with similar NPP. However, more research is needed to examine scaling errors in other heterogeneous areas and NPP errors associated with deriving LAI estimates.     

Development of an object-based framework for classifying and inventorying human-dominated forest ecosystems

This paper presents the development of a framework for classifying and inventorying Eastern US forestland based on the level of anthropogenic disturbance and fragmentation using high spatial resolution remote sensing data and a multiscale object-based classification system. We implemented the framework using a suburban area in Baltimore County, Maryland, USA as a case study. We developed a three-level hierarchical scheme of image objects. The object-based, multiscale classification and inventory framework provides an effective and flexible way of showing different mixes of human development and forest cover in a hierarchical fashion for human-dominated forest ecosystems. At the finest scale (level 1), the classification nomenclature describes basic land cover feature types, which are divided up into trees and individual features that fragment forests. The overall accuracy of the classification was 91.25%. At level 2, forest patches were delineated and classified into different categories based on the degree of human disturbance. At level 3, major roads were used to segment the study area into larger objects, which were classified on the basis of relative composition and spatial arrangement of forests and fragmenting features. This study provides decision makers, planners and the public with a new methodological framework that can be used to more precisely classify and inventory forest cover. The comparisons of the estimates of forest cover from our analyses with those from the 2001 National Land Cover Dataset (NLCD) show that aggregated figures of forest cover are misleading and that much of what is mapped as forest is highly degraded and is more suburban than natural in its land use.     

Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape

From its inception, land-use and land-cover mapping have been major themes in remote-sensing research and applications. Although frequently considered together, land use and land cover (LULC) are defined differently, with land use referring to the economic function of the Earth’s surface and land cover to its natural or engineered biophysical cover. Land cover can be observed directly using remote sensing, but land use must be inferred from the cover type. In this study, we test whether object-based image analysis (OBIA) can improve the land-cover and land-use classification in a complex agricultural landscape located along the border between Poland and Ukraine. We quantitatively compared the results of OBIA-based versus per-pixel classifications for both land cover and land use, respectively. Our results show that land-cover classification was not significantly improved when OBIA-based methods were used. Although overall classification accuracy was modest, land-use classification was significantly improved when OBIA-based methods were applied using both spectral and spatial/geometric features of image objects, but not when spectral or spatial/geometric features were used independently. Our results suggest that in anthropogenically altered landscapes where the geometry and arrangement of surface spatial structure may convey land-use information, use of OBIA-based techniques may provide a powerful tool for improving classification.     

多尺度超像素分割和奇异谱分析的高光谱影像分类

目的 高光谱影像（hyperspectral image,HSI）中“同物异谱,异物同谱”的现象普遍存在,使分类结果存在严重的椒盐噪声问题。HSI中的空间地物结构复杂多样,单一尺度的空间特征提取方法无法有效地表达地物类间差异和区分地物边界。有效解决光谱混淆和空间尺度问题是提高分类精度的关键。方法 结合多尺度超像素和奇异谱分析,提出一种新的高光谱影像分类方法,从而充分挖掘地物的局部空间特征和光谱特征,解决空间尺度和光谱混淆的问题,提高分类精度。利用多尺度超像素对影像进行分割,获取不同尺度的分割影像,同时在分割区域内进行均值滤波,减少类内的光谱差异,增强类间的光谱差异;对每个区域计算平均光谱向量,并利用奇异谱分析方法获取光谱的主要鉴别特征,同时消除噪声的影响;利用支持向量机对不同尺度超像素分割影像进行分类,并进行决策融合,得到最终的分类结果。结果 实验选取了两个标准高光谱数据集和一个真实数据集,结果表明,利用本文算法提取的光谱—空间特征进行分类,比直接在原始数据上进行分类分别提高约26.8%、9.2%和13%的精度;与先进的深度学习SSRN （spectral-spatial residual network）算法相比,本文算法在精度上分别提升约5.2%、0.7%和4%,并且运行时间仅为前者的18.3%、45.4%和62.1%,处理效率更高。此外,在训练样本有限的情况下,两个标准数据集的样本分别为1%和0.2%时,本文算法均能取得87%以上的分类精度。结论 针对高光谱影像分类中的难题,提出一种新的融合光谱和多尺度空间特征的HSI分类方法。实验结果表明,本文方法优于对比方法,可以产生更精细的分类结果。     

Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)-vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.     

ECOCLIMAP-II: An ecosystem classification and land surface parameters database of Western Africa at 1 km resolution for the African Monsoon Multidisciplinary Analysis (AMMA) project

This work is devoted to a presentation of the ECOCLIMAP-II database for Western Africa, which is an upgrade for this region of the former initiative, ECOCLIMAP-I, implemented at global scale. ECOCLIMAP-II is a dual database at 1-km resolution that comprises an ecosystem classification and a coherent set of land surface parameters. This new physiographic information (e.g. leaf area index, fractional vegetation cover, albedo and land cover classification), was especially developed in the framework of the African Monsoon Multidisciplinary Analysis (AMMA) programme in order to support the modelling of land-atmosphere interactions, which stresses the importance of the present study. Criteria for coherence between prevalent land cover classifications and the analysis of time series of the satellite leaf area index (LAI) between 2000 and 2007 constitute the analysis tools for setting up ECOCLIMAP-II. The LAI and inferred fraction of vegetation cover are spatially distributed per land cover unit. The fraction of vegetation cover is handled to split the land surface albedo into vegetation and bare soil albedo components, as is required for a large number of applications. The new ECOCLIMAP-II land cover product is improved with regard to the spatial coherence compared to former version. The reliability of the physiographic details is also confirmed through verification with land cover products at higher resolution.     

Improving global scale land cover classifications with multi-directional POLDER data and a decision tree classifier

Several investigations indicate that the Bidirectional Reflectance Distribution Function (BRDF) contains information that can be used to complement spectral information for improved land cover classification accuracies. Prior studies on the addition of BRDF information to improve land cover classifications have been conducted primarily at local or regional scales. Thus, the potential benefits of adding BRDF information to improve global to continental scale land cover classification have not yet been explored. Here we examine the impact of multidirectional global scale data from the first Polarization and Directionality of Earth Reflectances (POLDER) spacecraft instrument flown on the Advanced Earth Observing Satellite (ADEOS-1) platform on overall classification accuracy and per-class accuracies for 15 land cover categories specified by the International Geosphere Biosphere Programme (IGBP).

A set of 36,648 global training pixels (7 × 6 km spatial resolution) was used with a decision tree classifier to evaluate the performance of classifying POLDER data with and without the inclusion of BRDF information. BRDF ‘metrics’ for the eight-month POLDER on ADEOS-1 archive (10/1996–06/1997) were developed that describe the temporal evolution of the BRDF as captured by a semi-empirical BRDF model. The concept of BRDF ‘feature space’ is introduced and used to explore and exploit the bidirectional information content. The C5.0 decision tree classifier was applied with a boosting option, with the temporal metrics for spectral albedo as input for a first test, and with spectral albedo and BRDF metrics for a second test. Results were evaluated against 20 random subsets of the training data.

Examination of the BRDF feature space indicates that coarse scale BRDF coefficients from POLDER provide information on land cover that is different from the spectral and temporal information of the imagery. The contribution of BRDF information to reducing classification errors is also demonstrated: the addition of BRDF metrics reduces the mean, overall classification error rates by 3.15% (from 18.1% to 14.95% error) with larger improvements for producer's accuracies of individual classes such as Grasslands (+ 8.71%), Urban areas (+ 8.02%), and Wetlands (+ 7.82%). User's accuracies for the Urban (+ 7.42%) and Evergreen Broadleaf Forest (+ 6.70%) classes are also increased. The methodology and results are widely applicable to current multidirectional satellite data from the Multi-angle Imaging Spectroradiometer (MISR), and to the next generation of POLDER-like multi-directional instruments.     

基于时序MODIS反射率产品统计检验方法的土地覆盖更新

土地覆盖产品的生产是遥感领域的研究热点。全球范围土地覆盖产品因其在空间范围上的巨大尺度,生产周期较长,因此产品的时间跨度也很大,需要具有与之相对应的快速生成与更新技术,以提高产品的时间分辨率。使用MODIS历史土地覆盖产品和反射率产品,采用平均值显著性统计检验的思想,实现土地覆盖产品的快速更新。在MODIS条带号为h26v05的结果中选取宁夏自治区作为检验样区,通过目视检验和精度评价的方法对土地覆盖更新结果进行检验,总体精度为90.0290%。对水、混交林、草原、城市和建成区、裸地或低植被覆盖地变化的趋势和变化原因进行分析论证,表明采用平均值显著性统计检验的思想使用时序反射率产品对土地覆盖产品进行自动化快速更新是可行的。     

Application of the MODIS global supervised classification model to vegetation and land cover mapping of Central America

While mapping vegetation and land cover using remotely sensed data has a rich history of application at local scales, it is only recently that the capability has evolved to allow the application of classification models at regional, continental and global scales. The development of a comprehensive training, testing and validation site network for the globe to support supervised and unsupervised classification models is fraught with problems imposed by scale, bioclimatic representativeness of the sites, availability of ancillary map and high spatial resolution remote sensing data, landscape heterogeneity, and vegetation variability. The System for Terrestrial Ecosystem Parameterization (STEP) - a model for characterizing site biophysical, vegetation and landscape parameters to be used for algorithm training and testing and validation - has been developed to support supervised land cover mapping. This system was applied in Central America using two classification systems based on 428 sites. The results indicate that: (1) it is possible to generate site data efficiently at the regional scale; (2) implementation of a supervised model using artificial neural network and decision tree classification algorithms is feasible at the regional level with classification accuracies of 75-88%; and (3) the STEP site parameter model is effective for generating multiple classification systems and thus supporting the development of global surface biophysical parameters.     

Comparison of fuzzy c-means classification,linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels

Three different 'soft' classifiers (fuzzy c-means classifier, linear mixture model, and probability values from a maximum likelihood classification) were used for unmixing of coarse pixel signatures to identify four land cover classes (i.e., supervised classifications). The coarse images were generated from a 30m Thematic Mapper (TM) image; one set by mean filtering, and another using an asymmetric filter kernel to simulate Multi-Spectral Scanner (MSS) sensor sampling. These filters collapsed together windows of up to 11 11 pixels. The fractional maps generated by the three classifiers were compared to truth maps at the corresponding scales, and to the results of a hard maximum likelihood classification. Overall, the fuzzy c-means classifier gave the best predictions of sub-pixel landcover areas, followed by the linear mixture model. The probabilities differed little from the hard classification, suggesting that the clusters should be modelled more loosely. This paper demonstrates successful methods for use and comparison of the classifiers that should ideally be extended to a real dataset.     

A new combination classification of pixel- and object-based methods

This study presents a novel classification method using a combination of pixel- and object-based classifications, which includes the pixel-based uncertainty classification, object-based classification, and combined pixel- and object-based classification. As the spatial resolution increases, the complexity of land covers and the degree of information uncertainty in remote-sensing imagery increase, making remote-sensing image classification more difficult. For high-resolution image classification, using the pixel-based method it is easy to misclassify the different components with characteristic variations within the same land cover as different categories, and with the object-based method it is easy to misclassify the different categories of land cover with a strong spatial correlation as the same category. By using the proposed method, the pixel- and object-based classifications are performed on the image respectively, and the pixel-based classification result is utilized to correct the object-based classification result to obtain the optimized synthesis classification result. The experiments indicate that the combined classification method not only makes full use of the advantages of the individual-level methods, but also overcomes their disadvantages and produces higher classification accuracy than the single pixel- or object-based method. The accuracy improvement with the combined classification in the three experiments is 8.3, 9.5, and 13.2% relative to the pixel-based classification, and 7.2, 6.1, and 8.1% relative to the object-based classification.     

Possibilities and limitations of artificial neural networks for subpixel mapping of land cover

Although developments in remote sensing have greatly improved land cover mapping, the mixed pixel problem has not yet been fully addressed. Soft classification techniques have been introduced to address the problem, but they do not show the spatial location of the class proportions in a pixel. Subpixel mapping has been introduced to address the drawbacks of soft classifications. In this work, the feedforward backpropagating neural network (FFBPNN) was used for subpixel mapping. A set of class proportion images, which are to be treated as soft classification results, were created from a high spatial resolution (25 m) land cover dataset. For this purpose, the land cover dataset was aggregated both thematically (into two, four or eight land cover classes) and spatially (into proportion images with pixel sizes of 75, 150 and 300 m). This resulted in nine different combinations that were considered here as study cases. Several FFBPNNs were trained using these proportion images and the original land cover dataset (which was used as a target). Subsequently, the best networks were used to reconstruct high spatial resolution land cover maps of two heterogeneous areas in the south of The Netherlands. The overall accuracies obtained revealed that the networks were influenced by the spatial frequency, shape and size of the different land cover types. Moreover, it was revealed that most of the errors were on the class boundaries where highly mixed pixels are to be expected. The accuracies spanned a wide range of values depending on the complexity of the cases. Although it was not possible to exhaustively explore all network architectures, the results demonstrate the potential of the FFBPNN for subpixel mapping.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Object-oriented classification of land use/cover using digital aerial orthophotography

In automatic/semiautomatic mapping of land use/cover using very high resolution remote-sensing imagery, the major challenge is that a single class of land use contains ground targets with varied spectral values, textures, geometries and spatial features. Here we present an object-oriented strategy for automatic/semiautomatic classifications of land use/cover using very high resolution remote-sensing data. The strategy consists of character detecting, object positioning and coarse classification, then refining the classification result step by step. The strategy combines the form classification of the objects located on the same level by using spectral values, textures and geometric features with function classification by using spatial logic relationships existing among the objects on the same level or between different levels. Furthermore, it overcomes the problem of transformation from form classification to function classification and unifies land use classification and land cover classification organically. Such an approach not only achieves high classification accuracy, but also avoids the salt-and-pepper effect found in conventional pixel-based procedures. The borderlines of the classification result are clear, the patches are pure, and the classification objects exactly match the ground targets distributed across the study site. A feasible technical strategy for the large-scale application is discussed in this article.     

Change detection with heterogeneous data using ecoregional stratification, statistical summaries and a land allocation algorithm

A ca. 1980 national-scale land-cover classification based on aerial photo interpretation was combined with 2000 AVHRR satellite imagery to derive land cover and land-cover change information for forest, urban, and agriculture categories over a seven-state region in the U.S. To derive useful land-cover change data using a heterogeneous dataset and to validate our results, we a) stratified the classification using predefined ecoregions, b) developed statistical relationships by ecoregion between land-cover proportions derived from the 1980 national-level classification and aggregate statistical data that were available in time series for all regions in the U.S., c) classified multi-temporal AVHRR data using a process that constrained the results to the estimated proportions of land covers in ecoregions within a multi-objective land allocation (MOLA) procedure, d) interpreted land cover from a sample of aerial photographs from 2000, following the protocols used to produce the 1980 classification for use in accuracy assessment of land cover and land-cover change data, and e) compared land cover and land-cover change results for the MOLA method with an unsupervised classification alone. Overall accuracies for the 2000 MOLA and unsupervised land-cover classifications were 85% and 82%, respectively. On average, the 1980-2000 land-cover change RMSEs were one order of magnitude lower using the MOLA method compared with those based on the unsupervised data.     

Using genetic algorithms in sub-pixel mapping

In remotely sensed images, mixed pixels will always be present. Soft classification defines the membership degree of these pixels for the different land cover classes. Sub-pixel mapping is a technique designed to use the information contained in these mixed pixels to obtain a sharpened image. Pixels are divided into sub-pixels, representing the land cover class fractions. Genetic algorithms combined with the assumption of spatial dependence assign a location to every sub-pixel. The algorithm was tested on synthetic and degraded real imagery. Obtained accuracy measures were higher compared with conventional hard classifications.