EVALUATION OF THE THEMATIC INFORMATION CONTENT OF THE ASTER-VNIR IMAGERY IN URBAN AREAS BY CLASSIFICATION TECHNIQUES

Transcription

1 EVALUATION OF THE THEMATIC INFORMATION CONTENT OF THE ASTER-VNIR IMAGERY IN URBAN AREAS BY CLASSIFICATION TECHNIQUES T. G. Panagou a *, G. Ch. Miliaresis a a TEI, Dpt. of Topography, 3 P.Drakou Str., Thiva, 300, Greece (panagou, miliaresis)@ .com KEY WORDS: Landcover, Supervised Classification, Principal Component, U.S.G.S. Classification System ABSTRACT: The evaluation of the thematic information content of ASTER-VNIR with pixel size m and spectral resolution corresponding to green, red and infrared was performed. The aim was estimated the suitability of imagery for urban landcover mapping. Unsupervised and supervised classification techniques were used. The images were radiometrically corrected by non-parametric correction method based on principal components. The correction technique was aiming to destripe the images and to correct for atmospheric effects. 3 ground control points were used to geometrically correct the images by a polynomial of second order resulting to. m RMS. A terrain classification system was devised from the bibliography that included classes that could be mapped by image classification techniques from Landsat-TM imagery. A set of unsupervised classification techniques were implemented and the thematic classes derived were interpreted by fieldwork and photo-interpretation in attempt to determine the thematic information content of VNIR imagery. It was found that the following classes were mapped: water bodies, trees, grass (parks), bare ground, shadows, dark roofs, light roofs, dark road, light road. Finally, supervised classification (maximum likehood) was applied in a attempt to map with greater accuracy the thematic classes by including spatial information in the determination of their spectral signature. Thus, training areas were selected for each thematic class determined previously by the unsupervised classification methods. The ground truth data was collected by the fieldwork assisted by a handheld GPS. The accuracy of the resulted thematic map was evaluated by using the Kappa index of agreement.. INTRODUCTION ASTER is a cooperative effort between NASA and Japan's Ministry of International Trade and Industry (MITI) that obtains high resolution ( to 90 m) images of the Earth in the visible, near-infrared (VNIR), shortwave-infrared (SWIR), and thermal infrared (TIR) regions of the spectrum (TERRA, 003). ASTER data products include: spectral radiances and reflectances of the Earth's surface, surface temperature and emissivities, digital elevation maps from stereo images, surface composition and vegetation maps, cloud, sea ice, and polar ice products, etc. The VNIR subsystem operates in three bands (green, red and near-infrared) with m resolution and a 60 km swath width (ASTER, 003). Landcover mapping is of great importance since it is the first stage (landcover emissivity estimate) in the determination of biophysical parameters like temperature on the basis of the mid and thermal infrared Aster bands. Thus, the aim of the current research effort is to evaluate the thematic information content of ASTER VNIR subsystem in urban areas by determining the landcover classes that might be mapped by image classification techniques. This process is critical since VNIR sensor has higher spatial resolution and limited spectral resolution in comparison to Landsat Thematic Mapper Sensor (Lillesand and Kiefer, 97).. METHODOLOGY First the images were radiometrically and geometrically corrected and a terrain classification system was devised from the bibliography that identifies the landcover classes mapped by image classification techniques at scale : A set of unsupervised classification techniques was used in order to map the thematic information content of ASTER VNIR imagery while supervised classification techniques verified the potential mapping of the previously defined classes.. Data and Study Area The satellite image named pg-pra _007_0 was used. It was given for free from the US Geological Survey during the one-year evaluation period of ASTER. It was acquired on the th of December 00 and the processing level was A (no radiometric and geometric corrections were applied). The latitude and the longitude are within the range 37.7 to 3. and to.096 respectively. The study was implemented in the central portion of the image composed by 6 rows and 0 columns that covers the port of Piraeus and the municipality of Athens. Components Eigenvectors pca pca pca3 VNIR VNIR VNIR Eigenvalue Variance 9.6 %.90 % 3.0 % Table. Principal components analysis of bands VNIR-0, VNIR-0, VNIR-03.. Radiometric Correction A non-parametric radiometric correction method was implemented based on the principal components analysis was used (Eastman, 999). More specifically unstandardized

2 principal components analysis was applied on the collection of VNIR bands (Table ). The digital number that corresponds to a pixel in each band can be reconstructed as a linear combination of the three PCAs on the basis of equations (), () and (3). [VNIR-0]= (0.939*[pca])+(-0.63*[pca]) + (0.66*[pca3]) () [VNIR-0]= (0.939*[pca])+(-0.63*[pca])+ (0.66*[pca3]) () [VNIR-03]= (0.939*[pca])+(-0.63*[pca])+ (0.66*[pca3]) (3) From the table was concluded that the first two PCAs account for more than the 96% of the variance. It is assumed than the third PCA hold information relevant to striping and noise. Thus, if this component is removed and the rest of the components (pca and pca) reassembled according to the equations (), ( and (6), then the systematic and non systematic radiometric effects should be removed. area. A non-parametric method was used that was based to a second degree polynomial σαρωτής (Fukue et al., 996; Tokunaga and Hara, 996). 3 ground control points (GCPs) were measured on the ground with a GARMIN- hand-held GPS. The RMS was 0. m but after the drop of the three GCPs with the maximum error, the resulted RMS was equal to. meters. Note that the nearest neighbor resampling method was implemented. The RMS based estimation of the scale of the thematic maps derived from this dataset should be : The color composite of the georeferenced bands is given in figure.. Terrain Classification System The most well known terrain classification is that proposed by US Geological Survey (Lilleand and Kiefer 97). Such a system identifies the thematic classes that can be interpreted from specific satellite imagery. Additionally more detailed/specific systems were defined for urban areas (Gluch 00, Jurgens 00, Petrini, 00). The main thematic classes that could be derived by image processing techniques from satellite imagery with spatial resolution 30 meters, were combined in the table. Level Level Level 3. Water bodies [VNIR-0]= (0.939*[pca])+(-0.63*[pca]) () [VNIR-0]= (0.939*[pca])+(-0.63*[pca]) () non built environment. Vegetation.3 Bare ground.. grass.. trees [VNIR-03]= (0.939*[pca])+(-0.63*[pca]) (6). Shadow.. dark roof. Buildings.. light roof built environment. Road network..3 very light roof.. road dark.. road light.6 unclassified Table. Terrain classification system for urban areas describing the classes than can be interpreted by image classification techniques (either supervised or unsupervised).. Classes Defined by Unsupervised Classification Figure. False color composit (Red VNIR03, Green VNIR0, Blue VNIR03). Georeferencing The imagery was georeferenced in order to correct for geometric distortions and reorient the data in order to register with topographic maps of scale :0.000 available for the study In order to verify that the thematic classes given in table, unsupervised classification was implemented. In contrast to supervised classification where we tell the system about the character (training areas) of the thematic classes we are looking for, unsupervised classification requires no advance information about the classes of interest. Rather it examines the data and breaks it into the most prevalent natural groupings/clusters observed in the feature space. At the end the analyst identifies these clusters as landcover classes through a combination of familiarity with the region and ground truth visits. The underlying idea is that thematic classes that are not evident in Table could be revealed if they are evident in the dataset. The self organizing cluster analysis was applied (Eastman, 999) and 7 spectral classes were mapped. Their interpretation is given in table 3.

3 ID Characterization Interpretation 0 Water bodies Sea 0 Vegetation Dense forest mainly on the hill sides 03 Urban light Buildings with light roof response 0 Road light Small roads which appear to be lighter due to mixed pixels with the surrounding lighter buildings. 0 Shadow Dark regions associated with the either the shadow of the hills within Athens ot the shadow of the high buildings along major highways crossing Athens 06 Grass Terrain of football stadiums, small city parks with very few or without trees 07 Bare ground Very light bare ground 0 Urban dark Small buildings, dense urban fabric 09 Road dark Very wide asphalt roads Table 3. The clusters derived by the unsupervised classification and their interpretation. The spatial arrangement of the clusters identified indicates that two or more clusters could occupy a single thematic class. For example Syggrou Avenue a major highway crossing Athens is occupied by road-dark (6%), shadow (%) and road-light (0%). This is expected due to high buildings creating shadows across the avenue and mixed pixels at the border of the avenue where very light pedestrian passages are evident. In other cases the spatial arrangement od clusters indicates that if spatial criteria were included in the classification process then might more spatially homogenous thematic classes could be derived.. Classes Defined by Supervised Classification Training areas representative of each landcover type were selected first (Figure ). For each thematic class at least two or more training areas were digitized. The divergence matrix (Table ) was computed in order to verify the separation of clusters centers. sea vegetation Urban light Road light shadow Grass Bare gound Urban dark Road dark Μέσος διαχωρισµός Table. Divergence matrix (distance between the centroids of the training areas. 3 0 The maximum likelihood classification technique was implemented. Here the distribution of reflectance values in a training site are described by a probability density function, developed on the basis of Bayesian statistics. The classifier evaluates the probability that a given pixel will belong to a thematic class and classifies the pixel to the category with the highest probability of membership (Mather, 97). Figure. Polygons of the selected training areas. ID class pixels % 0 Water bodies Vegetation Urban light Road light 9. 0 Shadow.6 06 Grass Bare ground Urban dark Road dark Total: Table. Occurrence of thematic classes (spatial extent). Both the spatial extent per thematic class (number of pixels or percentage area occupied) and the thematic map derived are given in Table and Figure 3 respectively.

4 Figure 3. The nine thematic classes derived by the maximum likelihood supervised classification method.

5 . Accuracy Assessment In order to evaluate the thematic map derived by supervised classification (Figure 3) a stratified random sampling was designed and points per thematic class were sampled. The landcover recorded in the field (ground truth) was recorded by the aid of a handheld GPS. The corresponding confusion matrix was given in Table 6. The overall classification performance was 7% while the kappa coefficient (Lillesand and Kiefer, 97) equals to 0.7. sea vegetation Urban light Road light shadow Grass Bare gound Urban dark Road dark Χi Χ+ i Table 6. Confusion matrix. 3. CONCLUSION The greatest classification error observed for the classes 03 and 0. Class 03 is substituted by the classes 0 and 07 while class 0 is substituted by the 0, 03, 0 and 09 (Table 6). This is expected since the classes urban light, road light and bare ground present high reflectance while the divergence of between the corresponding training area centroids is minimum (Table ). The same happens for the second case in which classes with minimal reflectance are included. REFERENCES ASTER (accessed Jan. 003). Eastman J., 999. Idrisi 3: Guide to GIS and Image Processing, volume. Clark Labs, Worcester, 69 p. Fukue, K., H. Fujisada, & M. Tokunaga, 996. New approach to ASTER orthoimage generation. Proceedings, International Society for Optical Engineering, Infrared Spaceborne Remote Sensing IV, p Gluch R., 00. Ubran Growth Detection Using Merged Landsat TM and Spot-P Data. Proceedings, Remote Sensing of Urban Areas, Jurgens C. (Ed.), Regensburg, -3 June, p. Lillesand T., R. Kiefer, 97. Remote Sensing and Image Interpretation. Wiley & Sons, New York, 7 p. Mather P., 97. Computer Processing of Remotely Sensed Images. John Wiley & Sons, New York, 3 p. Jurgens C., (Editor), 00 Remote Sensing of Urban Areas. Proceedings, nd Int. Conf., Regensburg, -3 June. Petrini F.et al, 00. Mapping the Development of Urban Areas from Space Case Studies of Vienna, Belgrade and Shangai. Proceedings, Remote Sensing of Urban Areas, Jurgens C. (Ed.), Regensburg, -3 June, p. TERRA, (accessed Jan. 003) Tokunaga, M., S.Hara, 996. DEM accuracy derived from ASTER data.: Proceedings, 7th Asian Conference on Remote Sensing, p.j-7-n J-7-. It is observed that the greatest occurrence (60%) appears for the thematic class 03 (urban light). This is expected since Athens is occupied by high either white or light gray buildings while the vegetation appears to be minimum (Table ). If the occurrence of the thematic classes constituting the built versus the non-built environment (Table ) are summed then it is concluded that only the 7% of the area in Athens corresponds to the non-built environment. ASTER/VNIR imagery despite its low spectral resolution (three spectral bands) was proved capable of mapping the landcover classes defined in the terrain classification systems designed on the basis of Landsat Thematic Mapper imagery. It was found that the following classes were mapped: water bodies, trees, grass (parks), bare ground, shadows, dark roofs, light roofs, dark road, and light road