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 Fig. 4. Flowchart of GLP data fusion procedure for two multi-spectral images, whose scale ratio is p/q. | p indicates down-sampling by P-1P indicates up-sampling by p, that is interleaving a couple of adjacent samples along a row or a column with p — 1 zeroes. r p is the p—reduction low-pass filter with frequency cutoff at 1/p and unity DC gain. e p is the p-expansion filter with frequency cutoff at 1/p and DC gain equal top. A vector of weights {wi, l = 1,..., L} is introduced to equalize spectral contributions. bic), 11 (fifth-order), 15 (seventh-order), and 23 (eleventh-order) coefficients have been assessed (Aiazzi, 1997a). The term poly nomial stems from interpolation and denotes that filtering is equal to fitting an n—th order polynomial to nonzero samples. The 7- taps kernel is widespread to yield a bicubic interpolation. In the case p = 3, two kernels with 17 and 23 taps have been designed for pyramid generation. For p = 5, a kernels with 29 coeffi cients has been designed. It is noteworthy that half-band filters have the even order coefficients, except zeroth, all identically null (Crochiere, 1983). Analogously for filters with frequency cut-off at one p-th of bandwidth, coefficients whose order is a nonzero multiple of p are null as well. Thus, the half-band 23-taps filter has 13 nonzero coefficients, while the 23-taps filter with cut-off at 1/3 has 17 nonzero coefficients. The frequency responses of all the filters are plotted in Figure 3. Frequency is normalized to the sampling frequency fs which is known to be twice the band width available to the discrete signal. The filter design stems from a trade-off between selectivity (sharp frequency cut-off) and com putational cost (number of nonzero coefficients). In addition, the assumption of negligible aliasing allows to define the equivalent filters at the k-th pyramid layer of GP and LP (Ranganath 1991) and, hence, of GGP and GLP, which turn out to be actually a low-pass and band-pass structure, respectively. 3. GLP FUSION SCHEME The flowchart reported in Figure 4 describes the data fusion al gorithm for the general case of two image data sets, having differ ent numbers of spectral bands, or equal number but wavelengths not all the same, and ground scale ratio equal to p/q, that have been preliminarily registered to each other, or better to the same cartographic coordinate system. Note that fusion of two data sets involves two levels of GLP, i.e. base-band at level K — 1. Let Sj (1) , l = 1,..., L be the first data set made up of L multi- spectral observations having lower resolution and size M x N. Let S; (2) , / = 1,..., K be the second data set constituted by another multi-spectral image having spatial resolution higher by a factor p/q, but lower spectral resolution (K < L) and size Mp/q x Np/q. The goal is to obtain a third set of L multi- spectral images, S^\ each having the same spatial resolution as S (2) . The upgrade of 5 (1) to the resolution of S (2) is the GLP of S (2) calculated for k = 0. The images of the set S (1) have to be expanded by p/q, i.e. interpolated by p and then reduced by q, to match the scale of the data having finer spatial resolution. Then, the high-pass component from S (2) is added to the expanded l = 1,,L, which constitute the low-pass component, in order to yield either a spatially enhanced set or a spectrally enhanced set of multi-spectral observations, S\ z \ l = 1,..., L. Equalization of high-pass features before merging is recom mended for spatial enhancement, because the images to be fused may exhibit different contrast. Thus, the high-frequency compo nents of the data having the higher spatial resolution are weighted by the ratio between the square root of the variances measured on the base-bands. Applicative cases of interest may be: 3 : 1, for spatial enhance ment of Landsat TM (30 m) through SPOT-P (10 m) (Aiazzi, 1998), 3 : 2 for spectral enhancement of SPOT-XS (20 m) through LANDSAT TM, 5 : 3 for spectral enhancement of multi- spectral MOMS-2P (18 m) through Landsat TM. For spectral enhancement, a decision based on physical congruence must be embedded in the selection/combination of low-frequency coeffi cients from the data having higher spectral resolution. Thus, the weights wi may also be zero for certain couples of bands whose fusion is a non-sense.

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