MULTI-SCALE ANALYSIS OF C-BAND SAR DATA FOR LAND USE MAPPING ISSUES

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1 MULTI-SCALE ANALYSIS OF C-BAND SAR DATA FOR LAND USE MAPPING ISSUES Tanja Riedel, Christian Thiel and Christiane Schmullius Friedrich-Schiller-University, Earth Observation, Jena, Germany; ABSTRACT The availability of up-to-date and reliable land cover/use maps is of great importance for many earth science applications. Aim of this study was to systematically analyse the effect of spatial resolution of C-band SAR data on segmentation quality and land cover classification accuracy as well as to find robust SAR parameters for the detection of broad land cover classes (water, forest, urban areas and open land) at different scales. The investigation will contribute towards the assessment of the potential of the future Sentinel-1 mission for land applications. The test site is located in northern Thuringia, Germany, including mainly forested regions like the eastern part of the lower mountain range Harz as well as intensively used agricultural areas. From April to December 2005 SAR data were acquired continuously over the test site building up a comprehensive database. The degradation of spatial resolution from 30m to 60, 90, 120 and 150m respectively, was performed by common spatial domain techniques. A hierarchical, object-based classification scheme was applied to all simulated low resolution data. In this context, the suitability of SAR data for image segmentation purposes was demonstrated. By the degradation of spatial resolution a slight decline in segmentation quality was assessed. Further on, the investigations showed that texture measures extracted from the neighbouring grey-level dependence matrix are a valuable tool for urban area mapping at different scales. This approach is applicable for both ASAR APP and WSM data. An improvement of land cover products could be achieved by the combination of multiresolution ASAR APP and WSM data. INTRODUCTION Earth observation represents a unique cost-efficient method for large-area land cover mapping providing spatially consistent and multitemporal information. The availability of reliable and up-to-date land cover information is required for a multitude of applications ranging from regional to global scales such as land cover change studies, ecological monitoring, map updating, management and planning activities or the implementation and control of national and international treaties (i, ii). Objective of ESA s Sentinel-1 mission is to ensure the continuation of operational applications exploiting C-band SAR data within the framework of the GMES programme. The Sentinel-1 radar satellite is scheduled to be launched in The mission currently is designed to operate in four different imaging modes providing C-band SAR data with a spatial resolution ranging from 5x5 to 25x100 meters. Data acquisition will be performed in selectable dual polarization HH/HV or VV/VH (iii). Aim of this study was to systematically analyse the effect of spatial resolution on the generation of broad land cover maps as well as to demonstrate the power of multi-resolution C-band SAR data for such applications. METHODS The study area is located in the northern part of Thuringia, Germany (Figure 1). It includes the eastern part of the lower mountain range Harz characterized by rough topography. The region is dominated by vast forests and small villages often surrounded by grasslands. Further on, the study area comprises intensively used agricultural areas like the Goldene Aue east of 41

2 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover Nordhausen in the south of the test site. From April to December 2005 SAR data were acquired continuously over the test site building up a comprehensive time series (Figure 2). HH/HV-polarized ASAR APP data and ERS-2 data were recorded nearly simultaneously providing C-band data at all polarizatons. Figure 1: Landsat TM 5 image of the test site from April 21, 2005 (channel 5-4-3) Figure 2: EO-data base 2005 Land cover maps were generated applying an object-based, hierarchical decision tree classification scheme (Fig. 3). Before segmentation and classification, all SAR data were preprocessed including calibration and orthorectification. As parts of the test site are characterized by significant topography, the normalization procedure proposed by Stussi et al., 1995 (iv) was applied. Image objects were delineated using the multiresolution segmentation approach (v) implemented in the ecognition software. Figure 3: Decision tree Aim of this study is to analyse the impact of scale on the generation of land cover products. Common approaches for the degradation of spatial resolution of EO-data include filtering in spatial and frequency domain (vi,vii,viii). In the framework of this study a convolution filter with equal weights was used for the simulation of SAR images with 60, 90, 120 and 150 meter resolution, respectively. Regarding SAR data the impact of speckle on image degradation has to be considered. In order to minimize these effects the multitemporal speckle filter developed by S. Quegan was used. For a filter window size of 5x5 pixels an increase in the equivalent number of looks of homogeneous areas from 2.5 to 17.5 looks was achieved. This is a usual 42

3 measure for the speckle noise level in SAR images as well as for the performance of the speckle filtering process (ix). The impact of scale on image segmentation will be evaluated on base of visual inspections, quantitative analyses (x) and by a comparison of the achieved classification accuracies. For the quantitative analyses 20 clearly definable reference areas of varying size, form and contrast to the surrounding land cover types were selected (five per class). Each was compared geometrically to the segmentation results and the morphological features area, perimeter and shape index were calculated for both, the reference areas and the segmented objects. All segments were considered which overlap with the reference object to at least 50%. A good segmentation result is reached when for all of the mentioned indicators the overall difference between delineated image objects and reference areas is as low as possible. Thematic map accuracy was assessed by calculating the confusion matrix and the kappa coefficient on base of randomly distributed reference points (100 for each class). As reference information high resolution optical data (Quickbird and Hymap) and orthophotos were used. RESULTS & DISCUSSION Segmentation Image segmentation was performed on base of the degraded and the unfiltered and filtered SAR data, respectively. The results of the quantitative segmentation quality evaluation are listed in Table 1. As mentioned earlier, a good segmentation results is indicated by a low overall difference between segmentation result and reference areas for all morphological features. Generally the results indicated, that sufficient segmentation results could be achieved using SAR data only. Most critical is the accurate delineation of small forests surrounded by agriculturally used areas as well as the detection of villages inside the forest. The segmentation quality could be improved by the application of a multitemporal speckle filter. By the degradation of spatial resolution a slight decline in segmentation quality was found. Additionally, the number of objects which were not recognized increases. Not surprisingly, the exact border between different land cover types was reproduced more accurately using the high resolution data. However, the quantitative quality measures indicate that relative good classification results also could be achieved on base of the segmentation results for the degraded data. This hypothesis is supported by the visual inspection of the segmentation results and the achieved classification accuracies on base of the undegraded data (Table 2). The overall classification accuracy slightly decreases with reduced resolution. Especially for water bodies a significant decline in the producer accuracy was found. Correspondingly, the user accuracy of open land declines as most water areas were assigned to the open land class. For the other land cover categories, i.e. for forested and urban areas, no obvious dependence on the spatial resolution was found. Feature Extraction and Classification Objective of this paper is to analyse the effect of spatial resolution on the generation of land cover products. In this context the question will be addressed whether it is possible to apply the proposed object-based classification scheme to SAR data with different spatial resolution. The mean radar backscatter of the image segments is not altered significantly by the degradation process. Therefore, the thresholds of the decision tree based on the backscattering coefficient could be transferred to the degraded SAR data. The differentiation of forested and urban areas is performed on base of texture parameters. For the extraction of meaningful texture measures the use of HH-polarized data is recommended. Suitable parameters are: standard derivation, mean and entropy of the gray-level co-occurrence difference vector and the neighbouring gray level dependency matrix (NGLD) (xi). By the degradation process the textural information content of the SAR data is reduced. Nevertheless it will be analysed, which texture measure works best for all investigated resolutions. Texture parameters were extracted on base of the filtered and unfiltered datasets. The potential for the classification of 43

4 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover urban areas was assessed by an analysis of ten reference areas including small villages and low density residential areas such as districts with one-family houses and allotment gardens as well as forested areas with rough topography. Former studies showed that the classification of these areas is most problematic. The change in texture measure with decreasing spatial resolution is shown in Figure 3. A high potential of the NGLD matrix approach as well as of the standard derivation for the separation of forested and Table 1: Evaluation of image segmentation quality at different degradation levels (speckle filter window 5x5) unfiltered Average difference of area [%] Average difference of perimeter [%] Average difference of shape index [%] Number of objects not detected Overlap [%] Overestimation of object size [%] Table 2: Classification accuracies achieved for the different segmentation results (UA user accuracy, PA producer accuracy, OA overall accuracy) PA UA PA UA PA UA PA UA PA UA water forest urban open l OA kappa urban areas is indicated. Meaningful texture features could be extracted from filtered and unfiltered SAR data, whereas for the parameters derived on base of the co-occurrence grey level difference vector the use of the unfiltered data is recommended. For all texture measures except the NGLD matrix approach a decline with decreasing spatial resolution was observed. For the NGLD matrix approach the application of a constant threshold regardless image resolution seems possible. To test this hypothesis the degraded SAR images were classified using different texture measures for the differentiation of forested and residential areas and keeping all thresholds unchanged. The results demonstrated the potential of the NGLD matrix approach to classify urban areas at different scales. Contrary to the other texture measures, classification accuracy for residential areas remains nearly unchanged with decreasing spatial resolution (Figure 4). Furthermore, the classification result for urban areas was better than those achieved for the other texture measures using adapted threshold values. 44

5 (a) (b) (c) Figure 3: Potential of different texture measures to differentiate forest (green) and urban areas (red) at different scale: (a) NGLD matrix, (b) mean of GLDV, (c) standard derivation Figure 4: Classification accuracy for urban areas at different scales using the standard derivation and NGLD matrix with constant thresholds (PA producer accuracy, UA user accuracy) Further investigations demonstrated that the NGLD matrix approach is also a suitable tool to extract meaningful texture measures from ASAR WSM images with 150 meter spatial resolution. By the combination of ASAR WSM and APP data an improvement of the land cover maps could be achieved. Especially for forests and urban areas the classification accuracy enhances. Minimum Object Size Another question arising in the context of scale analyses is whether a minimum object size could be specified beyond which image objects could be detected with high accuracy. To analyse this several reference areas were selected on base of the LINFOS data (land information system of Thuringia based on the interpretation of aerial photographs) and Google Earth. For water bodies a relationship between image resolution and minimum object size was obtained. In this context not only the absolute size of an image object is of importance, but also its form expressed by the area-to-perimeter ratio. In 30m-resolution data nearly all water bodies characterized by an area-to-perimeter-ratio of at least 40 were detected. The smallest lake classified correctly was 1.7 ha in size. For SAR data with 150m resolution an area-to perimeter ratio of at least 60 is recommended. For the urban area class no minimum object size could be specified on base of this analysis, as the separability to other classes mainly depends on the characteristics of the village or city. For example, image objects with low building density such as one-family house districts and allotment gardens were often misclassified as forest. Contrary, the detection of small industrial areas (<< 0.5 ha) was possible. 45

6 Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover CONCLUSIONS The potential of multiresolution C-band SAR data for the mapping of broad land cover classes was demonstrated. The proposed object-based, hierarchical classification scheme is applicable for data with different spatial resolution. A significant impact of spatial resolution on classification accuracy was found for water bodies only. For the mapping of urban areas using radar data the potential of the NGLD-matrix was demonstrated. The derived texture parameter shows relatively stable classification results during the entire year and at different scales. The application to ASAR wideswath data is possible. The transferability of the proposed methodology to other regions in Germany/Europe and strategies for an automated threshold adjustment will be investigated in near future. A further focus of future activities will be on the fusion of multiresolution optical and SAR data. Beyond this, the generation of more detailed land cover maps is planned, especially for urban and agriculturally used areas. ACKNOWLEDGEMENTS The ENVISAT ASAR and ERS-2 data were provided courtesy of the European Space Agency (Category-1 Project C1P 3115). The Enviland project subproject scale integration - is funded by the German Ministry for Economy and Technology (MW) and the German Aerospace Centre (DLR) (FKZ 50EE0405). REFERENCES i Franklin S E & M A Wulder, Remote sensing methods in medium spatial satellite data land cover classification of large areas. Progress in Physical Geography, 26(2): ii Jensen, J. R., Remote sensing of the environment an earth resource perspective. (Prentice Hall, New Jersey). iii Attema E, G Levrini & M Davidson,. Sentinel-1 ESA s new European radar observatory. In: 2 nd International Workshop of The Future of Remote Sensing (Antwerp, Belgium, October 2006). iv Stussi N, A Beaudoin, T Castel & P Gigord, Radiometric correction of multiconfiguration spaceborne SAR data over hilly terrain. In: International Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications (Toulouse, France, October 1995), v Baatz M & A Schäpe, Multiresolution segmentation an optimization approach for high quality multi-scale image segmentation. In: Angewandte Geographische Informationsverarbeitung XII. (AGIT, Salzburg), vi Narayanan R M, M K Desetty & S R Reichenbach, Effect of spatial resolution on information content characterization in remote sensing imagery based on classification accuracy. International Journal of Remote Sensing, 23(3): vii Garestier F, P Dubois-Fernandez, X Dupuis & P Paillou, Resolution effects on polarimetric high resolution X band data. In: 2 nd International Workshop on Applications of Polarimetry and Polarimetric Interferometry (PolInSAR, Frascati), CD. viii Gupta R K, T S Prasad & D Vijayan, Upscaling aspects of multi-resolution satellite data in spatial and frequency domain. Advances in Space Research, 29(1): ix Mastriani M, New wavelet-based superresolution algorithm for speckle reduction in SAR images. International Journal of Computer Science, 1(4):

7 x xi Neubert M, H Herold & G Meinel, Evaluation of remote sensing image segmentation quality further concepts and results. 1 st International Conference on Object-based Image Analysis (OBIA, Salzburg), CD. Riedel, T, C Thiel & C Schmullius, An Object-Based and Automated Classification Procedure for the Derivation of Broad Land Cover Classes Using Multitemporal C-Band SAR Data. 2 nd International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS, Valencia, Spain, September 2006), CD. 47