Analiza metod klasyfikacji obszarów miejskich zobrazowanych na wysokorozdzielczych zdjęciach satelitarnych

• Autorzy: Bochenek Z.

Warianty tytułu

EN

Analysis of classification methods of urban areas registered on high-resolution satellite images

• Języki publikacji: PL

Abstrakty

PL

W artykule zostały przedstawione wyniki prac nad utworzeniem metody cyfrowego klasyfikowania obszarów miejskich z wykorzystaniem wysokorozdzielczych obrazów satelitarnych. Przebadano różne sposoby cyfrowej klasyfikacji obrazów: metodę spektralnego zmieszania, metodę wykorzystującą miary tekstury obiektu, klasyfikację obiektową oraz metodę bazującą na zastosowaniu sieci neuronowych. Przedmiotem badań był fragment aglomeracji warszawskiej zobrazowany na 3 typach obrazów satelitarnych: Landsat ETM+, Terra ASTER oraz QuickBird. W wyniku analiz sformułowano wnioski dotyczące optymalnego sposobu klasyfikowania obszarów miejskich pod względem zróżnicowania struktury miasta; w prezentowanej pracy była to metoda hybrydowa łącząca w sobie elementy analizy obiektowej oraz wykorzystania sieci neuronowych. Realizacja projektu badawczego (nr 4 T12E 043 26) została sfinansowana ze środków KBN.

EN

Results of the research works aimed at preparation of the optimal method for classifying urban areas on the basis of digital analysis of high-resolution satellite image s were presented in the article. Data from three different satellite systems: Landsat ETM+, ASTER and QuickBird were used these works. The following methods of digital analysis and classification of satellite images were investigated in the course of the works: - Spectrai mixture analysis, - Classification of urban areas with inclusion of texture measures, - Object-oriented analysis with the use of multi-resolution segmentation, fuzzy logic and membership functions, - Neural networks. As a results of the conducted works it was found, that hybrid method, combining elements of object-oriented analysis and neural networks, is optimal for classifying urban areas with adequate accuracy. Application of multi-resolution segmentation enables to distinguish homogeneous objects within urban areas and to determine their several spectral and non-spectral parameters. Neural Networks analysis allows to find optimal solution concerning type of network for classification and importance of various parameters used in the classification process. The mean accuracy of classification with the use of this method, with division of urban areas into 3 levels of built-up land density, reaches 85 %. The crucial for obtaining high accuracy, while classifying high-resolution satellite image, is to select properly parameters of segmentation and a set of features characterizing land cover classes, used in neural network analysis.

Słowa kluczowe

PL

obszar miejski; obraz wysokorozdzielczy; obraz satelitarny

EN

urban area; high resolution image; satellite image

• Wydawca: Instytut Geodezji i Kartografii
• Czasopismo: Prace Instytutu Geodezji i Kartografii
• Rocznik: 2006
• Tom: T. 52, z. 110
• Strony: 5--34
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Bochenek Z.

• Instytut Geodezji i Kartografii

Bibliografia

• [1] Chen J., Gong P., He C., Luo W., Tamural M., Shi P., 2002, Assessment of the urban development plan of Beijing by using a CA-based urban growth model. Photogrammetric Engineering & Remote Sensing, Vol. 68, No. 10, pp. 1063-1071.
• [2] Dennison P. E., Roberts D. A., 2003, Endmember selection for multiple endmember specral mixture analysis using endmember average RSME. Remote Sensing of Environment, Vol. 87, pp. 123-135.
• [3] Gong P. D., Marceau J., Howarth P. J., 1992, A comparison of spatial feature extraction algorithms for land use classification with SPOT HRV data. Remote Sensing of Environment, Vol. 40, pp. 137-151.
• [4] Herold M., Liu X., Clarke K. C., 2003, Spatial metrics and image texture for mapping urban land use. Photogrammetric Engineering & Remote Sensing, Vol. 69, No. 9, pp. 991-1001.
• [5] Hung M., Ridd M. K., 2002, A subpixel classifier for urban land cover mapping based on maximum likelihood approach and expert system rules. Photogrammetric Engineering & Remote Sensing, Vol. 68, No. 11, pp. 1173-1180.
• [6] Kavzoglu T., Mather P. M., 2003, The use of backpropagating artificial neural networks in land cover classification. International Journal of Remote Sensing, Vol. 24, No. 23, pp. 4907-4938.
• [7]Keramitsoglou 1., Sarimveis H., Kiranoudis C. T., Sifakis N., 2005, Radial basis function neural networks classification using very high spatial resolution satellite imagery: an application to the habitat area of Lake Kerkini (Greece). International Journal of Remote Sensing, Vol. 26, No. 9, pp. 1861-1880.
• [8] Paola J. D., Schowengerdt R. A., 1995, A review and analysis of backpropagation neural networks for classification of remotely sensed multispectral imagery. International Journal of Remote Sensing, Vol. 16, No. 16, pp. 3033-3058.
• [9] Pesaresi M., Benediktsson J. A, 2001, A new approach for the morphological segmentation of high resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 2, pp. 309-319.
• [10] Rashed T., Weeks R. J., Stow D., Fugate D., 2005, Measuring temporal compositions of urban morphology through spectral mixture analysis: toward a soft approach to change analysis in crowded cities. International Journal of Remote Sensing, Vol. 26, No. 4, pp. 699-718.
• [11] Ridd M. K., 1995, Exploring a V-I-S (Vegetation-Impervious Surface-Soil) model for urban ecosystem analysis through remote sensing: comparative anatomy of cities. International Journal of Remote Sensing, Vol. 16, No. 12, pp. 2165-2185.
• [12] Roberts D. A, Gardner M., Church R., Ustin S., Scheer G., Green R. O., 1998, Mapping chaparral in the Santa Monica mountains using myltiple endmember spectral mixture models. Remote Sensing of Environment, Vol. 65, pp. 267-279.
• [13] Small C., 2003, High spatial resolution spectral mixture analysis of urban reflectance. Remote Sensing of Environment, Vol. 88, pp. 170-186.
• [14] Stankiewicz K., 2004, Zastosowanie sieci neuronowych do klasyfikacji zdjęć mikrofalowych pozyskanych z satelitów ENVISAT i ERS. Prace Instytutu Geodezji i Kartografii, Tom L, zeszyt 106, str. 63-71.
• [15] Thomas N., Hendrix C., Congalton R. G., 2003, A comparison of urban mapping methods using high-resolution digital imagery. Photogrammetric Engineering & Remote Sensing, Vol. 69, No. 9, pp. 963-972.
• [16] Wu C., Murray A. T., 2003, Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing of Environment, Vol. 84, pp. 493-505.
• [17] Yang L., Xian G., Klaver J. M., Deal B., 2003, Urban land cover change detection through sub-pixel imperviousness mapping using remotely sensed data. Photogrammetric Engineering & Remote Sensing, Vol. 69, No. 9, pp. 1003-1010.