Image classification From ESA Advanced Training course on

Image classification From ESA Advanced Training course on

Image classification From ESA Advanced Training course on Land Remote Sensing by Mrio Caetano Sunday, March 01, 2020 www.randbe.es 1 Goals 1 From data to information: presentation of different mapping approaches 2 Most common problems in image classification and how to solve them e.g. mixed pixel problem, lack of normality of the training data, Hughes phenomenon 3 Most important advances in satellite image classification

e.g. from pixel to object, from hard to soft classifiers, from parametric to non-parametric classifiers www.randbe.es Land information extraction from satellite images Map of continuous variables Map of categorical variables Map of thematic classes Land cover maps Burned area maps Flooded maps Agriculture maps Forest maps Leaf area index

Biomass Tree volume Thematic remote sensing Image classification Quantitative remote sensing Modelling www.randbe.es The traditional approach for land cover mapping Image classification at pixel level Map of categorical classes www.randbe.es

Recent advances in satellite image classification 1. Development of components of the classification algorithm, including training, learning and approaches to class separation e.g. artificial neural networks, decision trees 2. Development of new systems-level approaches that augment the underlying classifier algorithms e.g. fuzzy or similar approaches that soften the results of a hard classifier, multiclassifier systems that integrate the outputs of several classification algorithms 3. Exploitation of multiple types of data or ancillary information (numerical and categorical) in the classification process e.g. use of structural or spatial context information from the imagery, use of multitemporal data, use of multisource data, use of ancillary geographical knowledge in the overall classification system Source: Wilkinson, 2005 www.randbe.es For many years the research emphasis has been on the classification step itself. Image classification at pixel level

Map of categorical classes Does it satisfy the user needs? New classification algorithms Recent research A new spatial unit of analysis Spatial analysis for map generalisation www.randbe.es Redefine the approach for thematic information extraction Thematic information extraction from satellite images 1 Definition of the mapping approach 2

Geographical stratification 3 Image segmentation 4 Feature identification and selection 5 Classification 6 Ancillary data integration 7 Post-classification processing 8

Accuracy assessment * * * * * mandatory www.randbe.es Thematic information extraction from satellite images 1 Definition of the mapping approach 2 Geographical stratification

3 Image segmentation 4 Feature identification and selection 5 Classification 6 Ancillary data integration 7 Post-classification processing 8 Accuracy assessment

* * * * * mandatory www.randbe.es 1. Definition of the mapping approach The mapping approach has to take into account, e.g. Characteristics of the satellite data to be used Technical specifications of the final map (e.g. MMU) Characteristics of the geographical area to be mapped Availability of ancillary data Definition of the spatial unit of analysis

Decision on stratifying the study area Decision on the use of ancillary data MMU = Minimum Mapping Unit www.randbe.es 1. Definition of the mapping approach Minimum Mapping Unit (MMU) The MMU is the smallest area that is represented in a map In vector maps the MMU is the smallest object that is represented in the map In raster maps the MMU usually is the pixel e.g. in the NLCD 2001 (USA) the MMU is 30x30 m pixel

NLCD = National Land Cover Database e.g. in the CORINE Land Cover (CLC) maps (from EEA) the MMU is 25 ha EEA European Environment Agency www.randbe.es 1. Definition of the mapping approach Spatial unit of analysis This is the unit to which the classification algorithms will be applied Image pixel Per pixel or subpixel classification Object Object oriented image classification

www.randbe.es 1. Definition of the mapping approach The selection of the spatial unit of analysis depends on: Spatial resolution of the satellite image Type of thematic information we want to extract, e.g. land cover, land use Format of the map we want to produce, i.e. vector or raster Minimum Mapping Unit of the final map Post-processing tasks that we are planning to apply www.randbe.es 1. Definition of the mapping approach The steps required to information extraction depend on the defined mapping approach: Map format = raster MMU = pixel size of input satellite data Feature selection > Image classification > accuracy assessment MMU > pixel size of input satellite data

Feature selection > Image classification > post-processing > accuracy assessment Map format = vector upscaling Spatial unit of analysis = image pixel Feature selection > Image classification > post-processing > accuracy assessment Spatial unit of analysis = object Generalisation + Raster to vector conversion Image segmentation > Feature selection > Image classification > post-processing > accuracy assessment Generalisation Generate the objects www.randbe.es Thematic information extraction from satellite images 1

Definition of the mapping approach 2 Geographical stratification 3 Image segmentation 4 Feature identification and selection 5 Classification 6 Ancillary data integration 7

Post-classification processing 8 Accuracy assessment * * * * * mandatory www.randbe.es 2. Geographical stratification Geographical stratification the study area is divided into smaller areas (strata) so that each strata can be processed independently. Five general concepts are useful in geographical stratification: economics of size,

type of physiography, potential land cover distribution, potential spectral uniformity, edge-matching issues. Data that can be used for geographical stratification Vegetation maps Slope Climate data Aspect Elevation Existent land cover/use maps www.randbe.es 2. Geographical stratification Geographical stratification used on the production of the US National Land Cover Database (NLCD) - 2001 Input data

83 Level III ecoregions developed by Omernik NLCD 1992 AVHRR normalized greenness maps Source: Homer et al. (2004) AVHRR - Advanced Very High Resolution Radiometer www.randbe.es Thematic information extraction from satellite images 1 Definition of the mapping approach 2 Geographical stratification * 3 4

Feature identification and selection 5 Classification 6 Ancillary data integration 7 Post-classification processing 8 Accuracy assessment * * *

* mandatory www.randbe.es 3. Image segmentation This step is only required if the spatial unit of analysis is the object. Segmentation is the division of an image into spatially continuous, disjoint and homogeneous regions, i.e. the objects. Segmentation of an image into a given number of regions is a problem with a large number of possible solutions. There are no right or wrong solutions to the delineation of landscape objects but instead meaningful and useful heuristic approximations of partitions of space. www.randbe.es 3. Image segmentation A type of segmentation that is very common is the multi-resolution segmentation, because of its ability to deal with the range of scales within a single image. Super-objects

Sub-objects www.randbe.es Thematic information extraction from satellite images 1 Definition of the mapping approach 2 Geographical stratification 3 Image segmentation 4 Feature identification and selection 5

Classification 6 Ancillary data integration 7 Post-classification processing 8 Accuracy assessment * * * * *

mandatory www.randbe.es 4. Feature identification and selection What type of features can we use for information extraction? Should we, for some reason, manipulate the feature space? How can we select the best features for class discrimination? Manipulation and selection of features are used to reduce the number of features without sacrifying accuracy www.randbe.es 4. Feature identification and selection Spectral measurements 1st order measurements From a single date (Unitemporal approach) From multiple dates (Multi-temporal approach Secondary measurements derived from the image

2nd order measurements Measurements of the spatial unit being classified Measurements related to the neighbourhood Quantification of the spatial variability within the neighbourhood Texture Spatial features Semantic relationships of a spatial unit with its neighbours Ancillary information This term is generally used for non-spectral geographical information Data from images with different characteristics can also be considered as ancillary information. The approaches used for multisensor data may fall within data fusion. www.randbe.es 4. Feature identification and selection 1st order measurements Unitemporal approach Multi-temporal approach

The production of the US National Land Cover Database (NLCD) 2001 is based on a multi-temporal approach It helps to discriminate classes with different phenology Irrigated and rain fed agriculture Permanent and deciduous forests Source: Homer et al. (2004) www.randbe.es 4. Feature identification and selection 2nd order measurements Measurements of the spatial unit being classified In the GLOBCOVER project (ESA) a set of newchannels based on the annual NDVI profile are derived. Source: Defourny et al. (2005) www.randbe.es

4. Feature identification and selection 2nd order measurements Measurements related to the neighbourhood (contextual information) Most mapping approaches operate at a pixel level, ignoring its context Contextual information and semantic relationships with neighbours is always used by photo-interpreters in visual analysis. Several attempts have been carried out to take into automatic classification the contextual information. Fractals Texture First order statistics in the spatial domain (e.g. mean, variance, standard deviation, entropy)

Second order statistics in the spatial domain (e.g. homogeneity, dissimilarity, entropy, angular second moment, contrast, correlation) www.randbe.es Geostatistics (e.g., variogram, correlogram, covariance function) 4. Feature identification and selection some considerations on object oriented image classification In object oriented image classification one can use features that are very similar to the ones used on visual image interpretation Shape and size of the objects Spectral homogeneity within objects Semantic relationships of a

spatial unit with its neighbours Before object oriented image classification there was the per-field classification. In this approach the objects are not extracted from the satellite image through segmentation but instead from an existent geographical data base with landscape units, i.e. fields. www.randbe.es 4. Feature identification and selection Ancillary information continuous e.g. elevation, slope, aspect categorical e.g. soil type, existent land cover maps US National Land Cover Database 2001 Source: Homer et al. (2007)

www.randbe.es Thematic information extraction from satellite images 1 Definition of the mapping approach 2 Geographical stratification 3 Image segmentation 4 Feature identification and selection 5 Classification

6 Ancillary data integration 7 Post-classification processing 8 Accuracy assessment * * * * * mandatory www.randbe.es

5. Classification Allocation of a class to each spatial unit of analysis (SUA) Image spatial space Map of categorical classes Band 2 Each SUA is represented by a vector, consisting of a set of measurements (e.g. reflectance) Definition of decision boundaries to separate classes Image feature space Band 1 Definition of the decision rule, i.e. the algorithm that defines the position of a SUA with respect to the decision boundaries

and that allocates a specific label to that SUA The word classifier is widely used as a synonym of the term decision rule www.randbe.es 5. Classification Data mining Artificial intelligence satellite image classification Computer sciences natural language processing syntactic pattern recognition search engines medical diagnosis bioinformatics cheminformatics stock market analysis

classifying DNA sequences speech recognition, handwriting recognition object recognition in computer vision game playing robot locomotion Pattern recognition Statistics Machine learning www.randbe.es 5. Classification Different possibilities to categorise classifiers Type of learning supervised

Number of outputs for each spatial unit Hard (crisp) unsupervised Assumptions on data distribution Parametric Non-parametric www.randbe.es Soft (fuzzy) 5. Classification Type of learning Supervised classification Unsupervised

classification Source: CCRS www.randbe.es 5. Classification Classic supervised classifiers Minimum distance Parallelepiped Maximum likelihood Source: Jensen (1996) www.randbe.es 5. Classification Some considerations on the training stage The training phase is decisive on the final results of image classification. In fact, in these phase we collect the data that will be used to train the algorithm.

The usual restrictions on sampling (cost, availability of data and accessibility) may lead to an inadequate sampling. In case of parametric classifiers the number of sample observations affect strongly the estimates of the statistical parameters. As the dimensionality of the data increases for a fixed sample size so the precision of the statistical parameters become lower (i.e., Hughes phenomenon). It is common that even mixed pixels dominate the image, only pure pixels are selected for training. However, this may lead to unsatisfactory classification accuracy. www.randbe.es 5. Classification Parametric classifiers Assumptions on data distribution e.g., maximum likelihood classifier Traditionally most classifiers have been grounded to a significant degree in statistical decision theory.

Nonparametric classifiers e.g., decision trees, artificial neural networks, support vector machines, nearest neighbour These classifiers rely on assumptions of data distribution. The performance of a parametric classifier depends largely on how well the data match the pre-defined models and on the accuracy of the estimation of the model parameters. They suffer from the Hughes phenomenon (i.e. curse of dimensionality), and consequently it might be difficult to have a significant number of training pixels. They are not adequate to integrate ancillary data (due to difficulties on classifying data at different measurement scales and units). www.randbe.es 5. Classification Non-parametric classifiers Artificial Neural Networks An ANN is a form of artificial intelligence that imitates some functions of the human brain.

An ANN consists of a series of layers, each containing a set of processing units (i.e. neurones) All neurones on a given layers are linked by weighted connections to all neurones on the previous and subsequent layers. During the training phase, the ANN learns about the regularities present in the training data, and based on these regularities, constructs rules that can be extended to the unknown data Source: Foody (1999) www.randbe.es 5. Classification Non-parametric classifiers Artificial Neural Networks ANN ANN Number of output labels Type of learning Supervised

Unsupervised Most common types of ANN Multi-layer perceptron with back-propagation Self-organised feature map (SOM) Hopfield networks ART (Adaptive Ressonance Theory) Systems www.randbe.es Hard Soft 5. Classification Non-parametric classifiers Artificial Neural Networks Advantages of ANN It is a non-parametric classifier, i.e. it does not require any assumption about the statistical distribution of the data. High computation rate, achieved by their massive parallelism, resulting from a dense arrangement of interconnections (weights) and simple processors (neurones), which

permits real-time processing of very large datasets. Disadvantages of ANN ANN are semantically poor. It is difficult to gain any understanding about how the result was achieved. The training of an ANN can be computationally demanding and slow. ANN are perceived to be difficult to apply successfully. It is difficult to select the type of network architecture, the initial values of parameters such as learning rate and momentum, the number of iterations required to train the network and the choice of initial weights. www.randbe.es 5. Classification Non-parametric classifiers Decision Trees DT are knowledge based (i.e. a method of pattern recognition that simulates the brains inference mechanism). DT are hierarchical rule based approaches. DT predict class membership by recursively partitioning a dataset into homogeneous subsets.

Different variables and splits are then used to split the subsets into further subsets. There are hard and soft (fuzzy) DT. Source: Tso and Mather (2001) www.randbe.es 5. Classification Non-parametric classifiers Decision Trees Advantages of DT Ability to handle non-parametric training data, i.e. DT are not based on any assumption on training data distribution. DT can reveal nonlinear and hierarchical relationships between input variables and use these to predict class membership. DT yields a set of rules which are easy to interpret and suitable for deriving a physical understanding of the classification process. DT, unlike ANN, do not need an extensive design and training. Good computational efficiency. Disadvantages of DT The use of hyperplane decision boundaries parallel to the feature axes

may restrict their use in which classes are clearly distinguishable. www.randbe.es 5. Classification Number of outputs for each spatial unit Hard (crisp) classification each pixel is forced or constrained to show membership to a single class. Soft (fuzzy) classification each pixel may display multiple and partial class membership. Veg. Water Bare soil Soft classification has been proposed in the literature as an alternative to hard classification

because of its ability to deal with mixed pixels. www.randbe.es 5. Classification The mixed pixel problem A presence of small, sub-pixel targets B presence of boundaries of discrete land cover classes C gradual transition between land cover classes (continuum) D contribution of areas outside the area represented by a pixel Source: Foody (2004) www.randbe.es 5. Classification The number of mixed pixels in an image varies mainly with: Landscape fragmentation Sensors spatial resolution

MERIS FR pixels www.randbe.es The mixed pixel problem 5. Classification The mixed pixel problem The problem of mixed pixels exist in coarse and fine resolution images: In course resolution images the mixed pixels are mainly due to co-existence in the same pixel of different classes. MERIS FR In fine resolution images the mixed pixels are mainly due to co-existence in the same pixel of different components (e.g., houses, trees). IKONOS www.randbe.es 5. Classification Hard classification

Decision rules 0 30 -> Water 30 - 60 -> Forest wetland 60 - 90 -> Upland forest Decision rules are defined as membership functions for each class. Fuzzy classification Membership functions allocates to each pixel a real value between 0 and 1, i.e. membership grade. But, wow can we represent the sub-pixel information? Source: Jensen (1996) www.randbe.es 5. Classification How can we represent the sub-pixel information?

Sub-pixel scale information is typically represented in the output of a soft classification by the strength of membership a pixel displays to each class. Veg. Water Bare soil It is used to reflect the relative proportion of the classes in the area represented by the pixel www.randbe.es 5. Classification How can we represent the sub-pixel information? Map with primary and secondary classes Entropy image The pixel value translates a degree of mixing (entropy is minimised when the pixel is associated with a single class and maximised when membership is partitioned evenly between all of the defined classes).

Hills diversity numbers image The pixel values provides information on the number of classes, the number of abundant classes and the number of very abundant classes. www.randbe.es 5. Classification Soft classifiers Most common soft classifiers Maximum likelihood classification Fuzzy c-means Approaches based on fuzzy set theory Possibilistic c-means Fuzzy rule based classifications Artificial neural networks www.randbe.es 5. Classification Soft classifiers

Classification Some considerations on uncertainty Maximum likelihood classifier (MLC) MLC is one of the most widely used hard classifier. In a standard MLC each pixel is allocated to the class with which it has the highest posterior probability of class membership. MLC has been adapted for the derivation of sub-pixel information. This is possible because a by-product of a conventional MLC are the posterior probabilities of each class for each pixel. The posterior probability of each class provides is a relative measure of class membership, and can therefore be used as an indicator of sub-pixel proportions. Some authors use the term Fuzzy MLC, to discriminate it from the (hard) MLC. Conceptually, there is not a direct link between the proportional coverage of a class and its posterior probability. In fact, posterior probabilities are an indicator of the uncertainty in making a particular class allocation. However many authors have find that in practice useful sub-pixel information can be derived from this approach. www.randbe.es 5. Classification Soft classifiers

The continuum of classification fuzziness In the literature the term fuzzy classification has been used for cases where fuzziness is only applied to the allocation stage which does not seem to be completely correct. If we apply the concept of fuzziness to all stages of image classification we can create a continuum of fuzziness, i.e. a range of classification approaches of variable fuzziness. Fully-fuzzy classification Completely-crisp classification Classification stages Dominant class Pixel is allocated to a single class Dominant class Training Individual class proportions Allocation

Membership grade to all classes Testing Individual class proportions www.randbe.es Source : Foody (2004) 5. Classification Spectral unmixing Spectral unmixing = spectral mixture modelling = spectral mixture analysis Spectral unmixing is an alternative to soft classification for sub-pixel analysis. Spectral unmixing is based on the assumption that spectral signature of satellite images results essentially from a mixture of a small number of pure components (endmembers) with characteristic spectra. If so, it is then possible to use a limited number of components so that mixtures of these component spectra adequately simulate the actual observations.

Source: Tso and Mather (2000) N DNc image radiance for band c Linear mixture models are the most common N ismodels the number of endmembers used in satellite image c n Fn is the relative fraction of endmember n analysis DNn.c is the endmember n inner radiance Ec residual fitting error n1 c c DN F DN

E 1 www.randbe.es 5. Classification Spectral unmixing A case study: urban mapping Lu and Weng (2004) used Spectral Mixture Analysis for mapping the Urban Landscape in Indianapolis with Landsat ETM+ Imagery. SMA was used to derive fraction images to three endmembers: shade, green vegetation, and soil or impervious surface Output of spectral unmixing Shade fraction Vegetation fraction www.randbe.es Soil or impervious surface fraction

5. Classification Spectral unmixing A case study: urban mapping Pasture and Agricultural lands = commercial + industrial The fraction images were used to classify LULC classes based on a hybrid procedure that combined maximum-likelihood and decision-tree algorithms. Source: Lu and Weng (2004) www.randbe.es Lu-Weng urban landscape model 5. Classification Sub-pixel classification

Super-resolution mapping Although classification at sub-pixel level is informative and meaningful it fails to account for the spatial distribution of class proportions within the pixel. Super-resolution mapping (or sub-pixel mapping) is a step forward. Super-resolution mapping considers the spatial distribution within and between pixels in order to produce maps at sub-pixel scale. Several approaches of super-resolution mapping have been developed: Hopfield neural networks Pixel-swapping solution (based on geostatistics) Linear optimization Markov random fields www.randbe.es 5. Classification Sub-pixel classification Super-resolution mapping Pixel-swapping solution this technique allows sub-pixel classes to be swapped within the same pixel only.

Swaps are made between the most and least attractive locations if they result in an increase in spatial correlation between sub-pixels. Source: Atikson (2004) www.randbe.es 5. Classification Multiple classifiers approach Rationale Different classifiers originate different classes for the same spatial unit There are several studies on the comparison of different classifiers There is not a single classifier that performs best for all classes. In fact it appears that many of the methods are complementary Combination of decision rules can bring advantages over the single use of a classifier In the multiple classifiers approach the classifiers should be independent. To be independent the classifiers must use an independent feature set or be trained on separate sets of training data. www.randbe.es

5. Classification Multiple classifiers approach How different the results from different classifiers can be? Maximum likelihood Artificial Neural Networks Decision tree Source: Gahegan and West (1998) www.randbe.es 5. Classification Multiple classifiers approach Methods for combining classifiers Voting rules The label outputs from different classifiers are collected and the majority label is selected (i.e. majority vote rule). There are

some variants, such as the comparative majority voting (it requires that the majority label should exceed the 2nd more voted by a specific number). Bayesian formalism It is used with multiple classifiers that output a probability. The probabilities for a spatial unit for each class resulting from different classifiers are accumulated and the final label is the one that has the greatest accumulated probability. Evidential reasoning It associates a degree of belief with each source of information, and a formal system of rules is used in order to manipulate the belief function. Multiple neural networks It consists on the use of a neural network to produce a single class to each spatial unit, fed with the outputs from different classifiers. www.randbe.es

5. Classification a summary on image classification spectral secondary measurements g Vector of features eographical describing a spatial unit (sub-pixel) pixel object The aim of pattern recognition is to establish a link between a pattern and a class label one to one hard classification

one to many soft classification known supervised classification unknown unsupervised classification www.randbe.es Thematic information extraction from satellite images 1 Definition of the mapping approach 2 Geographical stratification 3

Image segmentation 4 Feature identification and selection 5 Classification 6 * * * Ancillary data integration 7 Post-classification processing

8 Accuracy assessment * * mandatory www.randbe.es 6. Ancillary data integration Ancillary data can be integrated after image classification in order to improve the results. Post-classification sorting - application of very specific rules to classification results and to geographical ancillary data (e.g., elevation, slope, aspect) There are several strategies based on expert systems, rule based systems and knowledge base systems www.randbe.es Thematic information extraction from

satellite images 1 Definition of the mapping approach 2 Geographical stratification 3 Image segmentation 4 Feature identification and selection 5 Classification 6 Ancillary data integration

7 Post-classification processing 8 Accuracy assessment * * * * * mandatory www.randbe.es 7. Post-classification processing Post processing is required in two cases

The Minimum Mapping Unit of the very final map is larger than the spatial unit used in the classification The final map has a vector format and the Spatial Unit of Analysis was the pixel Map generalisation Raster to vector conversion Upscaling www.randbe.es 7. Post-classification processing The steps required to information extraction depend on the defined mapping approach: Map format = raster MMU = pixel size of input satellite data Feature selection > Image classification > accuracy assessment

MMU > pixel size of input satellite data Feature selection > Image classification > post-processing > accuracy assessment Map format = vector upscaling Spatial unit of analysis = image pixel Feature selection > Image classification > post-processing > accuracy assessment Spatial unit of analysis = object Generalisation + Raster to vector conversion Image segmentation > Feature selection > Image classification > post-processing > accuracy assessment Generalisation Generate the objects www.randbe.es 7. Post-classification processing

Semantic generalisation Semantic generalisation MMU = 1 pixel (30mx30m) MMU = 5 ha www.randbe.es 7. Post-classification processing Semantic generalisation MMU = 1 pixel (30mx30m) 1 MMU = 5 ha Shrubland Forest 2 Agriculture

Bare soil 3 www.randbe.es Thematic information extraction from satellite images 1 Definition of the mapping approach 2 Geographical stratification 3 Image segmentation 4 Feature identification and selection 5

Classification 6 Ancillary data integration 7 Post-classification processing 8 Accuracy assessment * * * * *

mandatory www.randbe.es 8. Accuracy assessment Accuracy assessment allows users to evaluate the utility of a thematic map for their intended applications. The most widely used method for accuracy assessment may be derived from a confusion or error matrix. The confusion matrix is a simple crosstabulation of the mapped class label against the observed in the ground or reference data for a sample set. www.randbe.es 8. Accuracy assessment Main steps Probability sampling is necessary if one

wants to extend the results obtained on the samples to the whole map. 1Selection of the reference sample sampling units sampling design 2 Response design 3 Analysis and estimation Probability sampling requires that all inclusion probabilities be greater than zero, e.g. one cannot exclude from sampling inaccessible areas or landscape unit borders. The definition of the response design depends on the process for assessing agreement (e.g., primary, fuzzy or quantitative). One has to take into account the known areas (marginal distributions) of each map category to derive unbiased estimations of the proportion of correctly mapped individuals.

Source: Stehman (1999) www.randbe.es 8. Accuracy assessment Overall accuracy: 86% Small uncertainty Moderate uncertainty Large uncertertainty But, where is the error? www.randbe.es Uncertainty mapping Goals 1 From data to information: presentation of different mapping approaches

2 Most common problems in image classification and how to solve them e.g. mixed pixel problem, lack of normality of the training data, Hughes phenomenon 3 Most important advances in satellite image classification e.g. from pixel to object, from hard to soft classifiers, from parametric to non-parametric classifiers www.randbe.es References Atkinson, P.M., 2004, Resolution manipulation and sub-pixel mapping, in S.M. de Jong and F.D. van der Meer (eds), Remote sensing image analysis including the spatial domain, Dordrecht: Kluwer Academic Publishers. Defourny, P., Vancutsem, C., Bicheron, P, Brockmann, C., Nino, F., Schouten, L., Leroy, M., 2006, GLOBCPVER: a 300m global land cover product for 2005 using ENVISAT MERIS Time Series, Proceedings of ISPRS Commission VII Mid-Term Symposium: Remote Sensing: from Pixels to Processes, Enschede (NL), 8-11 May, 2006 Foody, G. M., 2004, Sub-pixel methods in remote sensing, in in S.M. de Jong and F.D. van der Meer (eds), Remote sensing image analysis including the spatial domain, Dordrecht: Kluwer

Academic Publishers. Foody, G. M., 2002, Status of land cover classification accuracy assessment, Remote Sensing of the Environment, 80: 185-2001. Foody, G.M., 1999, Image classification with a neural network: from completely crisp to fully-fuzzy situations, in P.M. Atkinson and N.J. Tate (eds), Advances in Remote Sensing and GIS analysis, Chichester: Wiley&Son. www.randbe.es References Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan. 2004. Development of a 2001 National Landcover Database for the United States. Photogrammetric Engineering and Remote Sensing, 70 (7): 829-840 Homer, C. C. Huang, L. Yang, B. Wylie and M. Coan. 2004. Development of a 2001 National Landcover Database for the United States. Photogrammetric Engineering and Remote Sensing, 70 (7): 829-840 Jensen, J.R., 1996, Introductory digital image processing: a remote sensing perspective, Upper Saddle River, NJ: Prentice Hall, 2nd Ed. Lu, D. and Weng, Q., 2004, Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery, Photogrammetric Engineedring and Remote Sensing, 70 (9), pp. 10531062 Wilkinson, G.G., 2005, Results and implications of a study of fifteen years of satellite image classification experiments, IEEE Transaction on Geosciences and Remote Sensing, 43:3, 433-440 Stehman, S.V., 1999, Basic probability sampling designs for thematic map accuracy assessment, International Journal of Remote Sensing, 20: 24232441.

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