Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 5 FUSION OF VARIABLE SPATIAL / SPECTRAL RESOLUTION IMAGES

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: FUSION OF 18 m MOMS-2P AND 30 m LANDS AT TM MULTISPECTRAL DATA BY THE GENERALIZED LAPLACIAN PYRAMID. Bruno Aiazzi, Luciano Alparone, Stefano Baronti, Ivan Pippi

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 5. CONCLUDING REMARKS The flexibility of the GLP method has been shown here in a practical case concerning non-conventional satellite data. The versatility and effectiveness of the method had been previously demonstrated in previous works (Aiazzi, 1997a, 1997c, 1998), through extensive comparisons, both objective and subjective, with HPF and wavelets methods, carried out on Landsat TM and SPOT data. Comparisons with wavelet methods are not feasible for the present case, since to the best of our knowledge so far, no published schemes can properly handle a 5/3 scale ratio. In addi tion, the experiments reported here concern spectral enhancement and not spatial enhancement as in most of the literature. On the other hand, comparisons must be restricted among feature-based methods, since pixel-based methods (IHS, LHS and PCS) have been demonstrated to be poorer than the former methods almost one decade ago (Chavez, 1991; Pohl, 1998). Future efforts will be devoted to devise three-layers schemes, e.g. by adding a high- resolution panchromatic observation to the above test case. ACKNOWLEDGEMENTS This work was carried out under grants of the Italian Space Agency (ASI). REFERENCES Aiazzi, B., Alparone, L., Baronti, S., Cappellini, V., CarD, R., Mortelli, L„ 1997. A Laplacian pyramid with rational scale fac tor for multisensor image data fusion. Proceedings of the In ternational Conference on Sampling Theory and Applications SAMPTA’97, Aveiro, Portugal, June 16-19 1997, pp. 55-60. Aiazzi, B., Alparone, L., Baronti, S., Lotti, F., 1997. Lossless image compression by quantization feedback in a content-driven enhanced Laplacian pyramid. IEEE Transactions on Image Pro cessing, 6(6), pp. 831-843. Aiazzi, B„ Alparone, L., Baronti, S., CarD, R., Mortelli, L., 1997. Pyramid-based multisensor image data fusion. In: Wavelet Ap plications in Signal and Image Processing V, Proceedings SPIE Annual Meeting ’97, San Diego, CA, 30 July-1 August 1997, SPIE Vol. 3169, pp. 224-235. Aiazzi, B., Alparone, L., Baronti, S., CarD, R„ 1998. An assess ment of pyramid-based multisensor image data fusion. In: Image and Signal Processing for Remote Sensing V, Proceedings EU- ROPTO Series, Barcelona, Spain, 21-24 September 1998, SPIE Vol. 3500, pp. 237-248. Baronti, S., Casini, A., Lotti, F., Alparone, L., 1994. Content- driven differential encoding of an enhanced image pyramid. Sig nal Processing: Image Communication, 6(5), pp. 463^169. Blanc, P., Blu, T., Ranchin, T., Wald, L., Aloisi, R., 1998. Using iterated rational filter banks within the ARSIS concept for pro ducing 10 m Landsat multispectral images. International Journal of Remote Sensing, 19(12), pp. 2331-2343. Burt, P. J., Adelson, E. H., 1983. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, 31(4), pp. 532-540. Burt, P. J., 1984. The pyramid as a structure for efficient compu tation. Multiresolution Image Processing and Analysis, edited by A. Rosenfeld, Springer-Verlag, Berlin. Chavez, P. S., Sides, S. C., Anderson, J. A., 1991 Comparison of three different methods to merge multiresolution and multispec tral data: Landsat TM and SPOT panchromatic. Photogram metric Engineering & Remote Sensing, 57(3), pp. 295-303. Crochiere, R. E., Rabiner, L. R., 1983. Multirate Digital Signal Processing, Prentice Hall, Englewood Cliffs. Garguet-Duport, B., Girel, J., Chassery, J.-M., Pautou, G., 1996. The use of multiresolution analysis and wavelet transform for merging SPOT panchromatic and multispectral image data. Pho- togrammetric Engineering & Remote Sensing, 62(9), pp. 1057- 1066. Kim, M. G., Dinstein, I., Shaw, L., 1993. A prototype filter design approach to pyramid generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(12), pp. 1233-1240. Li, H., Manjunath, B. S., Mitra, S. K„ 1995. Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing, 57(3), pp. 235-245. Nunez, J., Otazu, X., Fors, O., Prades, A., Pala, V, Arbiol, R., 1999. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 37(3), Part I/П, Special Issue on Data Fusion, pp. 1204- 1211. Pohl, C, van Genderen, J. L., 1998. Multisensor image fusion in remote sensing: concepts, methods, and applications. Interna tional Journal of Remote Sensing, 19(5), pp. 823-854. Ranchin, T, Wald, L., Mangolini, M., 1996. Improving spatial resolution of images by means of sensor fusion. A general solu tion: the ARSIS method. In: Remote Sensing and Urban Anal ysis, edited by J. P. Donnay and M. Barnsley, Taylor & Francis, London. Ranganath S., 1991. Image filtering using multiresolution repre sentations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(5), pp. 426-440. Toet, A., 1989. Image fusion by a ratio of low-pass pyramid. Pat tern Recognition Letters, 9(4), pp. 245-253. Wald, L., Ranchin, T, Mangolini, M., 1997. Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images. Photogrammetric Engineering & Remote Sens ing, 63(6), pp. 691-699. Wilson, T. A., Rogers, S. K., Kabrisky, M., 1997. Perceptual- based image fusion for hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 35(4), pp. 1007-1017. Yocky, D. A., 1996. Multiresolution wavelet decomposition im age merger of Landsat Thematic Mapper and SPOT panchromatic data. Photogrammetric Engineering & Remote Sensing, 62(9), pp. 1067-1074. Zhou, J., Civco, D. L., Silander, J. A., 1998. A wavelet transform method to merge Landsat TM and SPOT panchromatic data. In ternational Journal of Remote Sensing, 19(4), pp. 743-757.

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