Weighting fidelity versus classified area in remote sensing classifications from a pixel and a polygon perspective

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1 Weighting fidelity versus classified area in remote sensing classifications from a pixel and a polygon perspective Pere Serra 1, Gerard Moré 2 and Xavier Pons 1,2 1 Department of Geography Edifici B, Campus de la Universitat Autònoma de Barcelona, Cerdanyola del Vallès (SPAIN). Tel.: ; Fax: pere.serra@uab.cat 2 Center for Ecological Research and Forestry Applications (CREAF) Edifici C, Universitat Autònoma de Barcelona, Cerdanyola del Vallès (SPAIN). Tel.: ; Fax: g.more@creaf.uab.cat; xavier.pons@uab.cat Abstract This paper summarizes the consequences in the area classified and in the thematic accuracy of being more or less conservative in a hybrid classifier. The most important parameter of that classification consists in fidelity (the introduction of the threshold proportion at which to accept a spectral class as being a part of a thematic category). Two options have been tested: the first less conservative, the second more conservative. These fidelities have been applied to ten Mediterranean crops and tested using error matrices. Thematic accuracies were quantified following the classical approach (number of pixels correctly classified), a polygon approach (number of polygons correctly classified) and, finally, area approach (area correctly classified). Results showed that the most restrictive fidelity produces less area classified but with more thematic accuracy when unclassified pixels are not included in the quantification of the accuracy. This fact occurred in all the options (pixel, polygon and area) although did not affect all the crops equally. Keywords: hybrid classifier, fidelity, producer s accuracy, Mediterranean crops, area classified 1 Introduction Most of remote sensing classification techniques include some parameters to make the algorithm more or less restrictive or conservative. Theoretically, being more conservative implies an increase in the thematic accuracy of the classified pixels but also a decrease of the total classified area. Meanwhile a lot of effort has been made to analyse thematic accuracy mainly using error matrices (Khorram, 1999), in most of the works related to remote sensing classifications the consequences of making the classifier algorithm more or less conservative have been scarcely studied. On the other hand, classical classifiers are based on a pixel-perpixel classification although sometimes the results are finally used to enrich pre-existing vector cartography (Aplin et al., 1999). Our classification method for discriminating ten Mediterranean crops has consisted in a hybrid classifier that has been giving very good results for some years. The procedure is based on two modules, one that uses an unsupervised classification followed by another that assigns spectral categories to thematic classes through spatial correspondence. This last module requires the introduction of two different parameters: fidelity and representativity., the most important parameter according to our experience, is the threshold proportion at which to accept a spectral class as being a part of a thematic class, in terms of the proportion of the spectral 406

2 class that is inside the thematic class. Increasing fidelity usually leads more robust results but a larger amount of unclassified pixels. In this paper two different crop maps were computed using two diverse fidelities, one more conservative and another less. As asserted before, confusion or error matrix is the most common tool used to quantify thematic accuracy (Foody, 2002). In the majority of works errors matrices are analysed at pixel scale where training pixels and test pixels are compared (Barbosa et al., 1996; De Wit et al., 2004). Nevertheless, other less common options exist as at parcel or field scale (polygon) where the comparison is made by training and test parcels (Aplin et al., 1999; Martinez et al., 2001) or, finally, comparing areas according to training and to test pixels or parcels (Congalton et al. 1998). In this work all the three options have been considered. For this reason, after applying confusion matrices at pixel scale, the final raster maps were crossed with a digital rural cadastre vector map. The enriched vector layer allowed obtaining confusion matrices at polygon scale. Finally, error matrices were quantified applying area comparison extracted from polygons. The objective of this paper is to examine the implications in ten Mediterranean crops when two different fidelities are applied and to obtain error matrices in pixels, in polygons and in area. Our hypothesis is that a more conservative fidelity increases thematic accuracy although decreases the total classified area and that the per-polygon classification is more accurate than a per-pixel classification due to the reduction of spurious pixels. 2 Study area and materials The study area belongs to the path 198 and row 31 of Landsat TM and it is located in the centre of Catalonia (North-East of Spain) comprising 348,533 ha (Figure 1). It is an area where irrigated herbaceous crops and dry permanent crops predominate. In order to follow temporal signatures of crops, a multitemporal approach was applied including seven images from year All the images used were acquired from a Landsat annual subscription being introduced the following: May 16, June 1 and 17, July 19, August 4, October 23 and November Figure 1 Study area in horizontal lines. 407

3 Once the original format and metadata of acquired images were introduced, the following step was the geometric correction using the procedure developed by Palà and Pons (1995). During the geometric correction, all Landsat images were resampled using the nearest neighbour to preserve the original image radiometry. Georeferencing was done using a mean of 26 Ground Control Points (GCPs) and 12 test points per image, showing an average RMS about 20 m. The second step was the radiometric correction, through which digital numbers were converted into reflectance values using the sensor calibration parameters and other factors such as atmospheric effects, solar incident angle accounting for the relief, etc. (Pons and Solé- Sugrañes, 1994). The resultant corrected images presented a coherent range of reflectances. In order to avoid, in the classification stage, spectral and radiometric confusions a mask was applied to select only agricultural covers and to eliminate the rest (urban areas, forest surface, etc.). The mask was obtained from the Mapa de Cobertes del Sòl de Catalunya (Land Cover Map of Catalonia; CREAF, 2006). This map is the result of photo interpretation of colour ortophotos from 2000s, distinguishing up 58 land-covers. In the case of agricultural landcovers only two permanent crops (olive trees and vineyards) and any herbaceous crop were identified. According to some agricultural studies and our field experience (Serra, 2003), the main herbaceous and permanent crops cultivated in the study area were: winter cereals, maize, alfalfa, rice, fruit trees, fallow land, olive trees, vineyards, other herbaceous crops and pastures. 3 Classification process and polygon enrichment As mentioned before, our hybrid classifier consists in two MiraMon (Pons, 2006) modules: ISOMM and CLSMIX. In the ISOMM module, clusters are formed by iterative assignments of n-dimensional pixels. These assignments are based on the minimum Euclidean distance of a pixel from all current cluster centroids. The initial set of centroids, the seeds, is obtained prior to the clustering run. The module presents three options for obtaining the initial seeds: i) along the multivariate diagonal calculated from all the input variables, ii) a random distribution in all the multivariate space, iii) a distribution based on a equidistant sample over the image (for example a seed every 50 pixels, etc.). After each iteration cluster centroids are updated to the centroid of all currently assigned pixels. One of the main characteristics of ISOMM is that admit a high number of input variables (hundreds). The main utility of this property is to allow the use of high temporal resolution satellite series and other topographic and climatic variables (Serra, 2005). The module allows obtaining an elevated number of statistical categories (32,767) that may be eliminated or modified by the user using two different parameters: the minimum Euclidean distance between two valid clusters and the minimum number of pixels per cluster in order to consider the cluster valid. In the former case, clusters are fused in a single category when the Euclidean distance is lower than a minimum value defined by the user, while in the latter case a cluster is eliminated if its total area is lower than a threshold established by the user. Finally, the module requires the introduction of the following parameters: the numbers of desired clusters, the maximum number of iterations before terminates and a threshold value for terminating the algorithm. In this work all the bands of all the original images mentioned above were introduced except the thermal band. Moreover, and in order to consider crop chlorophyll activity and water 408

4 content along the year, for each date two image transformations were included: the Normalised Difference Vegetation Index (NDVI) and the Tasseled Cap Wetness (TCW). The NDVI is the most commonly used index and it has been optimal in several studies to supply information about crops (Lyon et al., 2003), being often considered as a greenness index because it is an estimation of chlorophyll activity. The TC transformation was, originally, a linear transformation of Landsat MSS data that projects soil and vegetation information into a single plane in the multi-spectral data space (Kauth and Thomas, 1976). The application was extended to Landsat TM data by Crist et al. (1986). They found that the six bands of reflected data occupy three dimensions, defining planes of soils, vegetation and a third feature, called wetness (TCW) and related to canopy and soil moisture. Therefore, inside ISOMM module 56 bands were used (7 original images * 6 bands + 7 NDVIs and + 7 TCWs). In the second part of the classification process, CLSMIX assigns every spectral class to a thematic class using two different parameters: fidelity and representativity. On one hand, the threshold proportion at which to accept a spectral class as being a part of a thematic class in terms of the proportion of the spectral class that is inside the thematic class. For example, 0.9 will mean that if 90 or more of the spectral class inside the training areas is under a given category of these areas, then this spectral class will be assigned to this category. On the other hand, the threshold proportion at which to accept a spectral class as being a part of a category in terms of the proportion of the category that is formed by a given spectral class. For example, 1 will mean that if 1 or more of the category is formed by a given spectral class, this spectral class will be assigned to the category. When a given pixel is classified, the module chooses the category that has the most reasonable assignation: i) The spatial correspondence between the spectral class and the training areas of that category (the spectral class is inside the training area), ii) The spectral class is mainly inside this category (an important proportion of the spectral class belongs to the category) and iii) The spectral class is not a insignificant part of the category. Conversely, a pixel will remain unclassified if no training area covers pixels in the same spectral class or if, given the input thresholds, no spectral class is adequate for it: either the pixel belongs to a class that is split too much between two or more categories (no clear tendency of the spectral class) or the pixel belongs to a class that is poorly representative of the total area of any category (perhaps the spectral class is noisy). In this work two options were tested: a fidelity of 0.31 (namely 31 or more of the spectral class inside the training areas is under a given category) and another much more restrictive of 0.51 (more than the half of the spectral class inside the training areas is under a given category). Once the two crop maps were obtained, the next step was the integration of such information into a digital rural cadastre vector map. The elementary unit is the cadastral parcel. The methodology consisted in crossing crop maps and the digital rural cadastre vector map and assigning to each parcel the crop more present inside its boundaries (this is the mode option). This option could be modified using a threshold value, more or less high, as in Martínez et al. (2001) where a crop needed to occupy at least 80 of the total polygon surface. The consequences of such threshold value will be discussed in future work. 409

5 4 Results 4.1 Overall accuracy As asserted before, some examples of thematic accuracy per-pixel, per-field and per-area already exist. In this work all these three options were analysed. Overall accuracy at pixelparcel-area scale was computed by dividing the total correct (the sum of the diagonal) by the total number of pixels-parcel-area in the error matrix. Accuracy of individual categories includes producer s accuracy (PA) and user s accuracy (UA). In the former, the total number of correct pixels-parcels-area is divided by the total number of pixels-parcels-area of that category from the ground data (usually in columns). This accuracy indicates the omission errors. In the later, the total number of correct pixels in a category is divided by the total number of pixels that were classified in that category measuring the commission errors. The most usual threshold proposed to accept a remote sensing classification is 85 (Campbell, 2002). Overall accuracies of crop maps were above 85 in all the cases according to Table 1. Apparently an initial fidelity of 0.51 produces more errors but this is fictitious because after excluding NODATA values (produced by doubts in ClsMix) TA increases significantly by pixel Table 1 Overall accuracies of crop maps by pixel 0.31 by polygon 0.51 by polygon 0.31 by area (ha) 0.51 by area (ha) Total number of elements classified 8,604,879 7,469,101 73,662 73, , ,632.7 Total number used to quantify thematic accuracy including NODATA 57,260 57,260 2,811 2,811 12, ,579.3 Total number used to quantify thematic accuracy excluding NODATA 57,073 52,734 2,810 2,568 12, ,511.2 Number of success 50,527 48,672 2,527 2,414 11,375 10,842.9 Thematic accuracy including NODATA Thematic accuracy excluding NODATA Analysis of final results by crops is made considering PA (omission errors) as UA (commission errors) is not affected by NODATA values and differences between the two fidelities very slight. 410

6 4.2 Producer s accuracy at pixel, polygon and area Figure 2 shows the results in producer s accuracy (PA) for fidelity 0.31 and 0.51 considering and without NODATA values (unclassified pixels). In the case of alfalfa, rice, maize, winter cereals and vineyards results are very similar showing high percentage of agreement, not being affected by different fidelities. Nevertheless, olive trees improve the percentage with a fidelity less restrictive with or without considering NODATA. Conversely, fruit trees, other crops, fallow land and pastures improve their results using a more restrictive fidelity without considering NODATA errors PA_31 PA_51 PA_31_NODATA PA_51_NODATA W. cereals Figure 2 Producer s accuracy from two fidelities (0.31 and 0.51) considering and without NODATA values at pixel scale. Figure 3 shows PA results for fidelity 0.31 and 0.51 considering and without NODATA by polygons. In this case, results show that the most restrictive option clearly improve thematic accuracy (TA) of fruit trees, other crops, fallow land and pastures considering and without NODATA. Nevertheless, only fruit trees have an acceptable percentage of TA., rice, maize and winter cereals results followed having a high TA and were not affected by fidelity. 411

7 PA_31_pol PA_51_pol PA_31 NODATA_pol PA_51 NODATA_pol W. cereals Figure 3 Producer s accuracy from two fidelities (0.31 and 0.51) considering and without NODATA values at polygon scale. Finally, when areas were compared (extracted from polygons), the restrictive option without NODATA produced very similar TA improvements than in the pixel case, affecting fruit trees, other crops, fallow land and pastures PA_31_area PA_51_area PA_31_areaNODATA PA_51_areaNODATA W. cereals Figure 4 Producer s accuracy from two fidelities (0.31 and 0.51) considering and without NODATA values by area. 4.3 Producer s accuracy comparison by pixel-polygon-area Figure 5 shows the results from the comparison among pixel, polygon and area when NODATA are considered as errors. Results are very similar in the case of alfalfa, rice maize, winter cereals and olive trees. Nevertheless, with a less restrictive polygon option (0.31), fruit 412

8 trees and other crops presented a slight improvement compared with pixel and area options and in the case of vineyards clearly worse PA_31_pixel PA_31_pol PA_31_area Winter cereals Figure 5 Producer s accuracy from one fidelity (0.31) considering NODATA values as errors by pixel, polygon and area. With a restrictive option results of olive trees were unacceptable. At polygon scale vineyards presented worse TA compared with pixel and area options PA_51_pixel PA_51_pol PA_51_area Winter cereals Figure 6 Producer s accuracy from one fidelity (0.51) considering NODATA values as errors by pixel, polygon and area. When NODATA were excluded from the less conservative option, results were very similar to figure 5, showing that with this fidelity NODATA values are not significant. 413

9 PA_31 NODATA_pixel PA_31 NODATA_pol PA_31_NODATA_area Winter cereals Figure 7 Producer s accuracy from one fidelity (0.31) not considering NODATA values as errors by pixel, polygon and area. Nevertheless, in the restrictive fidelity the exclusion of NODATA affected significantly fruit trees, fallow land and pastures in polygon scale and area PA_51 NODATA_pixel PA_51 NODATA_pol PA_51_NODATA_area 2 Winter cereals Figure 8 Producer s accuracy from one fidelity (0.51) not considering NODATA values as errors by pixel, polygon and area. 5 Conclusions A more conservative fidelity initially presents a worse TA and less classified area if it is compared to a less conservative fidelity. After excluding NODATA values TA increases. In the case of polygons, the reduction in number of polygons is lower because with only a categorical pixel the polygon was classified. On the other hand, when fidelity is changed main differences occurred in PA or omission errors. Commission errors are not significantly affected by different fidelities. 414

10 The high overall accuracy of crop maps was due to some crops (alfalfa, rice, maize and winter cereals) because they were very well classified by CLSMIX (near 100 of TA) and for this reason they were not influenced by fidelities. had acceptable values only in the case of a fidelity of 0.51 and without considering NODATA values as error. This situation occurred in all the options (pixel, polygon and area). On the other hand, olive tree results were unacceptable; this crop was not discriminated in our images. With a less conservative fidelity results improved in all the three options, but not enough. at pixel scale were not affected neither by fidelity nor NODATA, being the percentage of agreement acceptable. Results at polygon scale showed a strong decrease of the percentage of agreement and this situation was present in both fidelities and with or without NODATA., fallow land and pastures presented an improvement with a fidelity of 5.1 and without NODATA both in pixels, polygons and area. For all of them the best results were obtained at polygon scale and the worse at pixel scale with the exception of others crops that were the area comparison. Therefore, the final conclusions are that when we are less conservative NODATA do not affect the results but in the case of being more restrictive the inclusion or exclusion of NODATA produces different results. When NODATA are excluded having a fidelity of 0.51, the percentage of accuracy increases except in the case of crops with a very poor accuracy (as olive trees in our case). Results at polygon scale and area are better than at pixel scale because they minimise spurious pixels. However, vineyards were an exception, probably due to their parcels of small size. With this exception the enrichment of digital cadastre with a restrictive fidelity seems a very good option to consider as a final product. References Aplin, P., Atkinson, P.M. and Curran, P.J., 1999, Fine spatial resolution simulated satellite sensor imagery for land cover mapping in the United Kingdom. Remote Sensing of Environment, 68, pp Barbosa, P.M., Casterad, M.A. and Herrero, J., 1996, Performance of several Landsat 5 Thematic Mapper image classification methods for crop extent estimates in an irrigation district. International Journal of Remote Sensing, 17, pp Campbell, J.B., 2002, Introduction to remote sensing. New York: The Guilford Press. Center for Ecological Research and Forestry Applications (CREAF), Mapa de Cobertes del Sòl de Catalunya. Available online at: (last accessed April 2006). Congalton, R.G., Balogh, M., Bell, C., Green, K., Milliken, J.A. and Ottman, R., 1998, Mapping and monitoring agricultural crops and other land cover in the Lower Colorado River Basin. Photogrammetric Engineering and Remote Sensing, 64, pp Crist, E. P., R. Laurin, and R. C. Cicone, 1986, Vegetation and soils information contained in transformed Thematic Mapper data. In Proceedings of IGARSS' 86 Symposium, Paris, pp Paris, European Space Agency. De Wit, A.J.W. and Clevers, G.P.W., 2004, Efficiency and accuracy of per-field classification for operational crop mapping. International Journal of Remote Sensing, 25, pp Foody, G.M., 2002, Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, pp Kauth, R.J. and Thomas, G.S., 1976, The tasselled cap a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In LARS: Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. West Lafayette, Perdue University Press, 4B-41-4B-51. Khorram, S., 1999, Accuracy assessment of remote sensing-derived change detection. Maryland: American Society for Photogrammetry and Remote Sensing. Lyon, J.G.; Ward, A.; Atherton, B.C.; Senay, G.; Krill, T., 2003, Remote sensing and GIS for site-specific farming. In GIS for water resources and watershed management, Lyon, J.G. (Ed.), pp , Boca Raton: CRC Press. 415

11 Martinez, C., Calera, A., 2001, Irrigated crop area estimation using Landsat TM imagery in La Mancha, Spain. Photogrammetric Engineering and Remote Sensing, 67, pp Palà, V., and Pons, X., 1995, Incorporation of relief in polynomial-based geometric corrections. Photogrammetric Engineering and Remote Sensing, 61, pp Pons, X. (2005). MiraMon. Geographic Information System and Remote Sensing software. Centre de Recerca Ecològica i Aplicacions Forestals, CREAF. Bellaterra. (last accessed April 2006). Pons, X. and Solé-Sugrañes, L., 1994, A simple radiometric correction model to improve automatic mapping of vegetation from multispectral satellite data. Remote Sensing of Environment, 48, pp Serra, P., Moré, G., Pons, X., 2005, Application of a hybrid classifier to discriminate Mediterranean crops and forests. Different problems and solutions. In Proceedings of the XII International Cartographic Conference, 9-16 July, A Coruña (Spain), CD-ROM. Serra, P., Pons, X. and Saurí, D., 2003, Post-classification change detection with data from different sensors. Some accuracy considerations. International Journal of Remote Sensing, 24, pp