Modified Maximum Likelihood Classifications of Urban Land Use: Spatial Segmentation of Prior Probabilities

Transcription

1 Modified Maximum Likelihood Classifications of Urban Land Use: Spatial Segmentation of Prior Probabilities Victor Mesev Lecturer in Geography School of Biological & Environmental Sciences, University of Ulster, Coleraine, BT52 1SA, N. Ireland Tel: Fax:

2 Abstract A technique for improving the classification of urban land use by modifying the standard maximum likelihood discriminant function within spatially segmented urban parameters is introduced. The methodology examines how population census data can be used to modify prior probabilities, not at the global or regional scales, but at the intra-urban local level. This involves spatial segmentation of a Landsat TM image by areal census collection boundaries from which prior probabilities are generated with respect to varying proportions of three housing types. Consistent improvements over classifications produced by standard equal prior probabilities are evident on four settlements in southwest England. Total absolute error is lower under modified prior probabilities, but no one housing type has consistently lower area estimation error. Evidence suggests that high density housing is under-estimated (less pixels classified), and conversely that low density housing is overestimated (more pixels classified). Keywords: prior probabilities, segmentation, maximum likelihood, urban classification INTRODUCTION Satellite images of urban surfaces remain one of the more difficult datasets to classify. The typically complex mixture and irregular arrangement of artificial and natural urban land cover types produce reflectance levels that are always the result of the interaction of more than one ground phenomenon. Allowing for variations in spatial resolutions, class descriptions and local conditions, the proportion of mixed pixels that represent typical urban areas frequently exceeds 90% of the image. As a result, perpixel classifications of optical earth observation satellite images representing urban areas have low accuracy levels, and have traditionally been consigned to somewhat large-scale distinctions of urban/non-urban land cover, built/non-built, and at best, categorical building types (Forster, 1985; Haack et al., 1997). However, if urban images are ever to be applied to practical urban monitoring, 2

3 management and planning activities, much more detail is needed on the structural morphology and functional use of land deemed urban. This paper will examine one such per-pixel classification technique the maximum likelihood (ML) discriminant function and in particular the Bayes modification. It will examine the benefits of implementing separate prior probability class membership classifications within segmented urban areas. The proposed methodology hinges on the ability of ancillary data to first spatially segment a satellite image representing an urban area, and then, within each segment, to identify and update the prior probabilities for separate ML classification. Ancillary data are any data that are beyond the spectral domain, and these types of data are most conveniently stored and handled by a geographical information system (GIS). Area estimates of class membership are determined by ancillary data and directly inserted into the ML classifier as prior probabilities for each individual spatial segment of the urban area. Research into the use of prior probabilities in multispectral image classification is not new. The Bayes modification to the standard maximum likelihood classifier was fully documented in early remote sensing texts (Swain and Davis, 1978; Haralick and Fu, 1983), as well as reported by Haralick et al. (1973) on the use of spatial information in a single band, and Kettig and Landgrebe (1976) who published a multivariate method. Strahler (1980) demonstrated improvements in classification accuracy of natural vegetation in Doggett Creek, California. Other more recent contributions include Skidmore and Turner (1988), who extracted class probabilities from grey-level frequency histograms; Maselli et al., (1992) who formalised a parametric/non-parametric link between the ML discriminant function and prior probabilities; Conese and Maselli (1992) who focussed on error matrices to modify area estimates; and Foody et al. (1992) who explored the potential of posterior probabilities in continuous class memberships. Work in the urban domain seems to have been largely neglected, undoubtedly because of the frequent heterogeneity of urban land use composition. However, it is exactly when classes are closely related that prior/posterior probability estimates should have the most effect (Mather, 1985; Mesev, 1998; Mesev et al., 2001). This paper builds on previous research in two directions first, by further exploring the performance of prior probabilities for improving urban classifications, and second, assessing whether prior probability classifications within a spatially 3

4 segmented image will result in higher thematic accuracy. The reasoning behind segmentation is to reduce spectral variance and increase homogeneity. If prior probabilities are to succeed and increase classification accuracy they need to operate within more localised parameters than hitherto been witnessed. Local, or intra-urban (within urban) segmentation represents more precise class membership from ancillary information, and therefore more accurate class prior probabilities. In addressing these two objectives, the paper contributes to research and development discussions on closer interaction between image processing and GIS, none more evident than in Geocarto International papers from 2000 and The ML-modified classifier is a per-pixel algorithm, which is conceptually straightforward, and relatively simple to implement (Tom and Miller, 1984). This paper does not attempt to challenge the well-established literature on Bayes modifications to the maximum-likelihood classifier. Instead, it aims to examine the possibility of increasing the performance of prior probabilities by creating more favorable conditions for their calculation. What is meant by favorable is in terms of spatially segmenting a remotely-sensed image by reliable urban data and thereby reducing spectral variation and generating more precise class memberships (Hutchinson, 1982). Alternative research into modifying classifiers take into consideration contextual, textural, and spatial properties (Barnsley and Barr, 1997) inherent in the image, while others implement statistical operators such as neural networks and fuzzy set principles. Per-pixel techniques, like the Bayes modification of the ML, are known as hard classifiers, which means that just one class is assigned to one pixel. In contrast, soft classifiers, including mixture models and fuzzy sets, allocate proportions of one or more classes per pixel. As pixels representing urban areas are virtually all composed of mixed surfaces, the difference between the two types of classifiers is a matter of scale, generalization, and classification scheme. The focus in this paper is on improving the performance of the popular Bayes modified ML classifier and not to debate the continuum of pixel representation (Fisher, 1997). Besides, given the severe spatial heterogeneity in the arrangement of urban structures, there is a case to move away from detail (soft classifiers) in favor of more aggregated or averaged pixels (hard classifiers). In other words, the breakdown of mixed pixels may produce too much information that cannot be easily categorised within a scheme: a situation of not seeing the wood for the trees. The classification scheme in this 4

5 paper has been kept to a coarse level and is certainly conducive to per-pixel classification. The three urban classes are variations of residential density, high, medium, and low, and are representative of variations in proportions of buildings to vegetation ratios, respectively. For instance, pixels representing mostly, if not all, buildings are classified as high residential density, and pixels representing sizeable proportions of vegetation in conjunction with buildings as low density residential. Moreover, the three residential density classes are comparable to information held by the UK Population of Census on the three types of British housing, namely detached, semi-detached, and terraced. This association creates a situation where the demonstration of the Bayes modification of the ML classifier can proceed within the framework of census-derived prior probabilities. MODIFIED MAXIMUM-LIKELIHOOD CLASSIFICATION The ML algorithm in remote sensing classification is parametric and depends on each class being represented by a Gaussian probability density function completely described by the mean vector and variance-covariance matrix using all available spectral bands, and if possible, ancillary information. Given these parameters, it is possible to compute the statistical likelihood of a pixel vector being a member of each spectral class (Thomas et al., 1987; Besag, 1986). The objective is to assign the most likely class w j, from a set of N classes, w 1,... w N, to any feature vector x in the image. A feature vector x is the vector (x 1, x 2,... x M ), composed of pixel values in M features (in most cases, spectral bands). The most likely class w j for a given feature vector x is the one with the highest posterior probability Pr(w j x). Therefore, all Pr(w j x), j [1.. N] are calculated, and w j with the highest value is selected. The calculation of Pr(w j x) is based on Bayes theorem, Pr ( w j x) = Pr ( x wj) Pr( wj) Pr ( x). (1) 5

6 On the left hand side is the posterior probability that a pixel with feature vector x should be classified as belonging to class w j. The right hand side is based on Bayes theorem, where Pr(x w j ) is the conditional probability that some feature vector x occurs in a given class: in other words, the probability density of w j as a function of x (Besag, 1986). Supervised classifications, such as the ML, derive this information from training samples. Often, this is done parametrically by assuming normal class probability densities and estimating the mean vector and covariance matrix. Alternatively, it is possible to use Markov random fields (Berthod et al., 1996), or non-parametric methods, such as k- Nearest Neighbour (knn). The standard knn methods directly implement a decision function based on the number of training pixels per class proportional to the prior probability (Fukunaga and Hummels, 1987; Therrien, 1989). This is the prior probability of the occurrence of w j irrespective of its feature vector, and as such is open to estimation by prior knowledge external to the remotelysensed image. External prior knowledge will typically include information on the distribution and relative areas covered by each class in feature space. This ancillary information is most readily generated from urban data derived from a geographical information system (Harris and Ventura, 1995; Mesev, 1998). It therefore follows that the accuracy of class priors is at best equal to the quality of GIS prior knowledge. In image classification terms, prior probabilities can be visualised as a means of shifting decision boundaries to produce larger volumes in M-dimensional feature space for classes that are expected to be large and smaller volumes for classes that are expected to be small. The denominator in (1), Pr(x) is the unconditional probability density which is used to normalise the numerator such that N Pr( x ) = Pr( x w j ) Pr( w j ). (2) j = 1 Typically, ML classifiers assume prior probabilities to be equal and assign each Pr(w j ) a value of 1.0. However, variations in prior probabilities can be an important remedy for the problem of spectrally overlapping classes. If a feature vector x has probability density values that are significantly different from zero for several classes, it is not inconceivable for that pixel to belong to any of these classes. 6

7 When selecting a class solely on the basis of its spectral characteristics, a large probability of error frequently results (Conese and Maselli, 1992; Steele et al., 1998). The use of appropriate prior probabilities, based on reliable ancillary information, is one way to reduce this error in class assignments. Moreover, it would seem intuitively more sensible to suggest that some classes are more likely to occur than others. Local Prior Probabilities At this stage of the discussion, it is important to differentiate between global, individual, and local priors. Many proprietary software packages allow the use of global prior probabilities, where the user is expected to estimate them simply by using information on the anticipated (relative) class areas. The improvement on classification is often limited (Mather, 1985). At the other extreme, a vector of prior probabilities can be calculated for each and every individual pixel. This, however, is pointless, because if the correct prior probabilities for each individual pixel were known beforehand, the classification would not be necessary (Mesev et al., 2001). Given these problems, a compromise scale somewhere between global and individual can be derived by first subdividing the image into strata, or segments, according to ancillary context data, and then finding the local prior probability vector for each stratum. In the examples, extraneous data are used to stratify, and segment a satellite image according to some contextual rules to produce housing classes. Housing data are used from the 1991 UK Census of Population at the smallest collection unit (these are enumeration districts which represent, on average, around 250 households within urban areas) to infer local prior probabilities of housing type. Figure 1 here MODIFICATION OF PRIOR PROBABILITIES 7

8 Study Areas Spatially stratified and prior probability modified Bayes ML classifiers are applied to images of five cities and towns in the south west of England. These are Bristol, Swindon, Bath, and Taunton, with populations of 584,289, 164,245, 88,495 and 57,028, respectively. The aim is to classify a Landsat TM 7 multispectral image (path 203/ row 024), taken on the 30th April 2000, into three residential density categories: low, medium and high, which are associated with detached, semi-detached and terraced, respectively, for each settlement. Class area estimates for each urban land use generated with Bayes modifications are then compared to classifications generated using the standard equal prior probability maximum likelihood technique. Any improvements in accuracy will be measured by a convergence towards known housing proportions from the 1991 population census. The temporal differences between the Landsat image and the population census are unavoidable but do not represent a weakness in the methodology. Remember, the aim is to compare unequal with equal priors not the exact extent of the housing morphology. The software used is primarily UNIX-based ERDAS (Imagine 8.3), along with purpose-written programs written in the C++ language. Table 1 here Method In the United Kingdom, housing is readily differentiated into three categories, detached, semidetached, and terraced. These are essentially surrogates for the target classes of low, medium, and high density residential, respectively. Although this relationship is in part dependent on the size of buildings and size of gardens, there is still a strong correlation between types of properties and densities of properties. In table 1, typical housing densities for each category are calculated from the 1991 U.K. Census of Population, and expressed as the number of dwellings per hectare (see implementation). Prior probabilities are based on these densities and implemented at the local level. For prior probabilities to function most efficiently they need to operate within inclusive feature space and derive mutually exclusive classes. This essentially means that for the classification of mutually 8

9 exclusive housing types, an image must only be composed of housing segments. The assumption is that new commercial spatial resolution satellite images can be routinely segmented into urban and non-urban, as well as housing and non-housing. Within the housing feature space prior probabilities of the surrogate housing types detached, semi-detached, and terraced may be generated by census data and inserted into the ML classifier to produce the spectral classes of low, medium, and high density housing. This is exemplified by the city of Bristol in figure 1 which shows the Landsat image as a true color composite with housing proportions calculated at selected individual census ED (tract) level. Prior probabilities are determined as follows. Consider z k as the census variable housing type (where k: 1 = detached, 2 = semi-detached, and 3 = terrace). When stratified into exclusively housing feature space, the three classes will have pixels with feature values x i, where x 1,... x A are not necessarily mutually exclusive. The objective is to find the probability that a random pixel (within the housing type stratum of the image) will be a member of a spectral class w j (where j: 1 = low, 2 = medium, 3 = high), given its density vector of observed measurements x, in m-dimensional feature space and that it belongs to ancillary class z k, described as ( w, ) Pr x. (3) j z k It is also assumed that the effects of z k are external to the original generation of the mean vector and covariance matrix of w j. As a result the likelihood function Pr(w j x) is unaltered by the introduction of z k, but is simply modified by the conditional probability Pr( wj zk ). (4) This is a process of identifying the association between spectral class w j with census variable z k. For example, the spectral class labelled, as low density housing would be directly associated with a conditional probability of the census variable, detached dwellings. In effect, w 1 is weighted by the probability of z 1, producing the prior probability of Pr(w 1 ). In the empirical examples prior 9

10 probabilities of each of the three dwelling types are assumed to exist in inclusive m-dimensional feature space, so that ( ) + Pr( w ) + Pr( w ) 1. 0 Pr w =. The probability densities d i1 = Pr(x i w 1 ), d i2 = Pr(x i w 2 ), d i3 = Pr(x i w 3 ) are known for each pixel. Let l i1 be the shorthand for the posterior probability Pr(w 1 x i,z 1 ) that pixel i belongs to class w 1, and let p j be the shorthand for the prior probabilities. The Bayes modified ML is now represented as l d p = i1 1 i1 di 1p1 + di2 p2 + di3 p. (5) 3 Likewise, l i2 = Pr(w 2 x i, z 2 ), and l i3 = Pr(w 3 x i, z 3 ) may also be found, and of course, the sum of the three posterior probabilities equals 1.0, l ij = 3 d j = 1 ij d p ij j p j. (6) Implementation Before the modified ML classifier is implemented, a series of hierarchical segmentations are carried out in order to partition the image and generate the housing stratum from which the three housing types are ultimately derived. Segmentations are applied within standard unsupervised classifications using ERDAS ISODATA algorithm. These produce generalised urban and non-urban strata, from which the urban stratum is subdivided into built-up and non-built-up. The built-up stratum is then segmented into housing and non-housing. Table 2 here Using the housing stratum, prior probabilities for each housing type are calculated at the local level. This means that area proportions for each of three housing types (detached, semi-detached, and 10

11 terraced) are calculated for each of the census enumeration districts that represent a settlement. In Figure 1, enumeration districts (e.g. 09DDFA01) from the city of Bristol are illustrated along with their respective areal proportions of housing types. These proportions are normalised to create a probability distribution, and modified to take into account their relative size ratios (average prior probabilities are given in table 2). The size ratio transformation helps to preserve the relative areal proportions of each housing type, where for instance detached housing occupy larger physical space than terrace dwellings. Using stereoscopic aerial photographs, 20 samples of dwelling type sizes are generated and average relative size ratios between dwelling types constructed. The ratios stabilised at 1 detached dwelling to 1.5 semi-detached, and 1 detached to 2.25 terrace. Although these are approximations, they are still more realistic than assuming absolute 1:1 linear relationships. The entire classification process is illustrated in Figure 2. Figure 3 here Figure 4 here Results Prior probabilities for all three housing types are entered into the ML classifications at the stratified enumeration district (local) level. Results in figures 3 and 4 are based on comparisons of class area estimates of classifications of the three housing types generated by equal and stratified unequal prior probabilities. Figure 3 shows area estimates for most urban land use classes produced from the Bayes modified-ml classifier to be closer to those derived from the size-ratio transformed census figures. Total absolute error in all settlements is consistently lower under conditions of unequal as opposed to equal prior probabilities. However, in terms of housing, there are considerable variations between types and across the five settlements. No one housing type has consistently lower area estimation error but there is some evidence to suggest that high density housing is under-predicted (i.e. less pixels classified), and conversely low density housing is over-predicted (i.e. more pixels classified). The 11

12 reason for this may lie in the highly concentrated nature of British housing in central areas of towns. The spatial extent of individual houses around the central core may sometimes be much smaller than the spatial resolution of the satellite sensor. However, what becomes apparent from these results is that classifications are highly site specific, and they underline the immense problems that arise when subresidential classifications are attempted. For example, consider the case of Bristol in figure 4 where only the north western part of the city is shown for clarity. A visual comparison of urban land use coverage between equal priors (figure 4a) and unequal priors (figure 4b) reveals interesting patterns. More pixels have been classified as detached and semi-detached housing under conditions of equal prior probabilities, particularly across the more affluent parts of the west and north east of the sample area. At the same time, more pixels are classified as terraced under unequal prior conditions. In both cases, class estimates from unequal (stratified and modified) priors are closer to census estimates, and this is indicative of the flexibility of modified prior probabilities and their ability to incorporate spatial information from beyond the spectral domain. Individual census tracts can further highlight differences in classification between equal and unequal priors. The census tracts 09DDGH21 and 09DDFA01 demonstrate how lack of prior knowledge has resulted in the mis-classification of detached dwellings. In the case of the census tract (09DDFZ29) within the city centre detached housing is again classified using equal priors despite the fact that none are recorded in the census. This zero prior probability is included within the stratified and modified method and results in no pixels classified as detached. The testing procedure is essentially circular: classifications using census-derived prior probabilities are tested against census distributions. However, the objective of this paper is not to exclusively test equal with unequal priors but also to observe the relative performance of the Bayes classifier across the three land use types. In this sense, what the results reveal is that total absolute error is lower under modified prior probabilities, but that no one housing type has consistently lower area estimation error. However, there is some evidence to suggest that high density housing is underestimated (less pixels classified), and conversely that low density housing is over-estimated (more pixels classified). Further accuracy assessments of other settlements, including one with a detailed 12

13 accuracy evaluation, can be found in Mesev (1998), with all indicating slight to moderate improvements. CONCLUSIONS The Bayes classifer has a long history in environmental remote sensing but much shorter in urban mapping. This is the unfortunate consequence of dealing with a range of surfaces that can exhibit variable levels of complex spatial heterogeneity. Indeed, it is precisely when classes are spectrally overlapping that probabilistic and flexible ML modifications are essential. The importance of the Bayes classifier in urban areas is however not mirrored by the level of research. There seems to be a lack of methodological development with urban data and more specifically with census data, the most obvious source for prior probability modification. This paper has contributed to the development of the Bayes classifier for urban applications in two ways. The first is on strengthening the link between socio-economic data from the census with spectral patterns inherent in a satellite image. Area estimates of separate yet spectrally similar classes are readily available from sources such as the Census of Population, and when accommodated within the Bayes classifier can significantly improve classification accuracy. The second contribution is on spatial segmentation which is essential if local, not global, priors are to be calculated accurately. Improvements in classification accuracy have been reported in all of the empirical examples. However these are relatively small and further testing of the methodology is necessary on sensor data at much finer spatial resolutions to see whether standard per-pixel algorithms can contribute to the interpretation of high-volume, more detailed representations of urban morphology. One way to operationalise the methodology in a practical sense is to link probabilistic information from modified ML classifications directly within a GIS. For instance, posterior probabilities of per-pixel class membership may be stored and updated in a GIS database, and used to resolve land use planning, and land use change queries. However, this development is still at the theoretical stage. The maximum likelihood classifier is simple yet robust enough to accommodate modifications. With the advent of 13

14 commercial very high spatial resolution sensor data the ML classifier may be appropriate for many urban management applications. The technique needs to be examined within the context of the next generation of very high spatial resolution commercial satellite sensors. Data from these sensors are high volume and measure large spectral variations in urban land cover. In the absence of classifiers designed to deal with such data, simplicity in the maximum likelihood can accommodate large datasets, and the modifications outlined in this paper can improve the partitioning of urban feature space. Commercial ventures are now or are on the verge of generating remotely-sensed images at unprecedented sub 4 m spatial resolutions (examples include EOSAT Space Imaging IKONOS-2 at 1 m panchromatic, 4 m multispectral; EarthWatch Quickbird 0.61 m pan, 2.5 m multispectral; Orbimage OrbView-3 at 1 m, 4 m multispectral; and Russian SPIN-2 KVR-1000 at 2 m panchromatic) (Aplin et al., 1997; Jensen and Cowen, 1999). At last, these should facilitate more detailed urban classifications, but at a cost of increased data storage and slower transfer, along with a new set of noise-induced complications, such as the level of detail in the image exceeding the scope of interpretation. However, if advances in software engineering keep pace, there is great potential in using such fine resolution remotely-sensed data for urban applications, as long as classification schemes are kept at levels of detail that are manageable and make practical sense. REFERENCES Aplin, P., P.M. Atkinson, and P.J. Curran, Fine spatial resolution satellite sensors for the next decade, International Journal of Remote Sensing 18: Barnsley, M.J., and S.L. Barr, Distinguishing urban land-use categories in fine spatial resolution land-cover data using a graph-based, structural pattern recognition system, Computers, Environment and Urban Systems, 21:

15 Berthod, M., Z. Kato, Z, S. Yu, and L. Zerubia, Bayesian image classification using Markov random fields, Image and Vision Computing, 14: Besag, J., Towards Bayesian image analysis, Journal of the Royal Statistical Society, 48: Conese, C., and F. Maselli, Use of error matrices to improve area estimates with maximum likelihood classification procedure, Remote Sensing of Environment, 40: Fisher, P The pixel: a snare and a delusion, International Journal of Remote Sensing, 18: Foody, G.M., N.A. Campbell, N.M. Trodd, and T.F. Wood, Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification, Photogrammetric Engineering and Remote Sensing, 58: Forster, B.C., An examination of some problems and solutions in monitoring urban areas from satellite platforms, International Journal of Remote Sensing, 6: Fukunaga, K., and D. Hummels, Bayes error estimation using Parzen and k-nn procedures, IEEE Transactions on Pattern Analysis and Machine Intelligence, 9: Haack, B., S. Guptill, R. Holz, S. Jampoler, J.R. Jensen, and R. Welch, Urban analysis and planning, Manual of Photographic Interpretation. ASPRS: Bethesda, MD, Harris, P.M., and S.J. Ventura, The integration of geographic data with remotely sensed imagery to improve classification in an urban area, Photogrammetric Engineering and Remote Sensing, 61: Haralick, R.M., and K. Fu, Pattern recognition and classification, In Manual of Remote Sensing, Colwell, R. (ed.) ASPRS: Falls Church. Haralick, R.M, K. Shanmugam, and I. Dinstein, Textural features for image classification, IEEE Transactions on Systems, Man, and Cybernetics, SMC-3, 6: Hutchinson, C.F., Techniques for combining Landsat and ancillary data for digital classification improvement, Photogrammetric Engineering and Remote Sensing, 48: Jensen, J.R. and D.C. Cowen, Remote sensing of urban/suburban infrastructure and socioeconomic attributes, Photogrammetric Engineering and Remote Sensing, 65:

16 Kettig, R.L., and D.A. Landgrebe, Classification of multispectral data by extraction and classification of homogeneous objects, IEEE Transactions on Geoscience Electronics, GE-14, 1: Maselli, F., C. Conese, L. Petkov, and R. Resti, Inclusion of prior probabilities derived from a nonparametric process into the maximum-likelihood classifier, Photogrammetric Engineering and Remote Sensing, 58: Mather, P.M., A computationally-efficient maximum likelihood classifier employing prior probabilities for remotely-sensed data, International Journal of Remote Sensing, 6: Mesev, V., The use of census data in urban image classification, Photogrammetric Engineering and Remote Sensing, 64: Mesev, V., B. Gorte, and P.A. Longley, Modified maximum-likelihood classification algorithms and their application to urban remote sensing, In J-P. Donnay, M.J. Barnsley, and P.A. Longley, (eds.) Remote Sensing and Urban Analysis, London: Taylor & Francis, Skidmore, A.K., and B.J. Turner, Forest mapping accuracies are improved using a supervised nonparametric classifier with SPOT data, Photogrammetric Engineering and Remote Sensing, 54: Strahler, A.H., The use of prior probabilities in maximum likelihood classification of remotelysensed data, Remote Sensing of Environment, 10: Steele, B.M., J.C. Winne, and R.L. Redmond, Estimation and Mapping of Misclassification Probabilities for Thematic Land Cover Maps, Remote Sensing of Environment, 10: Swain, P.H., and S.M. Davis, Remote Sensing: The Quantitative Approach, McGraw-Hill: New York NY, USA. Therrien, C.W., Decisions, Estimation and Classification, Wiley, Chichester. Thomas, I.L., V.M. Benning, and N.P. Ching, Classification of Remotely-Sensed Images, IOP, Bristol. Tom, C.H., and L.D. Miller, An automated land-use mapping comparison of the Bayesian maximum likelihood and linear discriminant analysis algorithms, Photogrammetric Engineering and Remote Sensing, 50:

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18 Table and Figure captions Table 1 Housing density categories for each of the five settlements in west England. Table 2 Average prior probabilities for the three housing types (with scaling factors). Figure 1 Samples of local prior probabilities displayed alongside a Landsat TM 7 natural colour composite for the city of Bristol. Local prior probabilities are generated from housing data recorded by UK Census of Population and are represented by census collection units called enumeration districts (egs 09DDFA01, 09DDGH21, and 09DDFZ29). Figure 2 Insertion of prior probabilities in the maximum likelihood classifier. Data from the population census are used to stratify, train, and modify prior probabilities. Figure 3 Accuracy results from maximum likelihood classifications using equal and unequal prior probabilities on a Landsat TM 7 image representing four settlements in southwest England: (a) Bristol, (b) Swindon, (c) Bath, (d) Taunton. Area estimation errors (shaded bars) for each three housing types are calculated with respect to census numbers. Standard errors are shown by linear bars. Figure 4 Maximum likelihood classification results using (a) equal prior probabilities and (b) unequal prior probabilities for the city of Bristol. Note the differences in the three housing types between equal and unequal priors in the three highlighted census tracts. In all these cases the equal prior probability classification has over-predicted (or over classified) detached housing (low density) and under predicted semi-detached and terraced (medium and low density). This is most apparent for 09DDFZ29 where no detached housing exists. Under unequal prior conditions, detached and semi-detached are also over-predicted but this time terraced is very close to census estimates. 18

19 Table 1 Housing density categories for each of the five settlements in west England. Settlement Housing Density Categories (dwellings/ha.) low density medium density high density Bristol < Swindon < Bath < Taunton < Average <

20 Table 2 Average prior probabilities for the three housing types derived from census estimates (with scaling factors). Settlement Average Census Prior Probabilities low density medium density high density total Bristol Swindon Bath Taunton Scaling

21 Figure 1 21

22 Stratification GIS data Census of Population probability surfaces Census attributes features v training samples v probability density maps v * * prior < < * < likelihood maps Σ Σ Σ Σ Σ Σ Σ Σ v > > > area Figure 2 22

23 (a) Bristol high density medium density low density equal priors absolute=24.2 unequal priors absolute= area estimation error (% points) Figure 3a 23

24 (b) Swindon high density medium density low density low density equal priors absolute=22.8 unequal priors absolute= area estimation error (% points) Figure 3b 24

25 (c) Bath high density medium density low density equal priors absolute=19.8 unequal priors absolute= area estimation error (% points) Figure 3c 25

26 (d) Taunton high density medium density low density equal priors absolute=11.3 unequal priors absolute= area estimation error (% points) Figure 3d 26

27 Figure 4 27

28 Figure 4b 28