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 116 FUSION OF 18 m MOMS-2P AND 30 m LANDS AT TM MULTISPECTRAL DATA BY THE GENERALIZED LAPLACIAN PYRAMID Bruno Aiazzi 1 , Luciano Alparone 2 , Stefano Baronti 1 , Ivan Pippi 1 ^‘Nello Carrara” Institute on Electromagnetic Waves IROE-CNR, Via Panciatichi, 64, 50127 Firenze, Italy, baronti@iroe.fi.cnr.it “Department of Electronic Engineering, University of Firenze, Via S. Marta, 3, 50139 Firenze, Italy, alparone@lci.die.unifi.it KEYWORDS: Data Fusion, Multispectral Images, Landsat TM, MOMS-2P, Multiresolution Analysis, Generalized Laplacian Pyramid ABSTRACT Goal of this work is to present a general and formal solution to the problem of synergetic integration of multi-sensor image data, which may be collected with practically whatsoever spectral and ground resolution, although scale ratios larger than, say, 10, may be questionable in certain applicative contexts. The proposed data fusion methodology is applied to a specific example concerning observations from two different multi-spectral satellite sensors: Landsat TM (30 m resolution) and MOMS-2P (18 m ). The fusion procedure relies on the generalized Laplacian pyramid (GLP), which is a non-octave band-pass analysis structure unconstrained from the ground scales of the imaged data. The advantage is that the base-band extracted from the finer image exactly matches both in displaying scale and in resolution, i.e. content of spatial frequencies, the coarser image. For any rational scale ratio, a unique low-pass filter is needed. The filter design, however, is easy and noncritical for performances. The GLP scheme is superior in enhancing spatial features while retaining multi-spectral signatures, according to both objective and subjective quality criteria. The experiment reported highlights the assets of the pyramid approach: 5:3 fusion is designed for spectral enhancement of MOMS-2P (three 18 m bands) through Landsat TM (six 30 m bands). Since the three available multi-spectral bands of MOMS-2P overlap with Bands 2, 3, and 4 of TM, the test consists of synthesizing the three missing MOMS-2P bands -one in the blue and two in the infrared wave-lengths- from the TM data, in order to obtain 18m R-G-B true colour and three 18 m bands in near-medium infrared. 1. INTRODUCTION The ever increasing availability of space-borne sensors imag ing in a variety of ground scales and spectral bands makes multi sensor fusion of multi-spectral data a discipline to which more and more general formal solutions to a number of applications are demanded. Since physical constraints impose a trade-off between spatial and spectral resolution, spatial enhancement of poor-resolution multi-spectral data is desirable, as well as spec tral enhancement of data collected with adequate ground resolu tion but poor spectral selection. After data registration and resampling to obtain a homoge neous spatial scale, image data fusion may be approached at three levels of increasing hierarchy (Pohl, 1998): • Pixel-level combination: - deterministic: Intensity-Hue-Saturation (IHS) transform; - statistical: Principal Components Substitution (PCS). • Feature-level combination: - High-Pass Filtering (HPF) method (Chavez, 1991); - ARSIS method based on wavelet transform (Ranchin, 1996); - Generalized Laplacian pyramid (GLP) (Aiazzi, 1998). • Decision-level combination of pixel values and/or features based on previously extracted information. In applications of classification, however, merging algorithms are often requested to maintain the spectral characteristics of the original data (Wald, 1997). In this light, the HPF method is more efficient than pixel-level algorithms (IHS and PCS) in preserv ing spectral features of spatially enhanced bands (Chavez, 1991). HPF consists of an injection of high-frequency components into resampled versions of the multi-spectral data. Such components are extracted by taking the difference between a higher resolution observation and its low-pass version originally obtained through a box filter, i.e. a square digital filter with equal coefficients. The idea of HPF was developed in a formal multi-resolution framework by employing the wavelet transform (WT) as analysis-synthesis tool (Garguet-Duport, 1996; Li, 1995; Nunez, 1999; Yocky, 1996; Zivco, 1998). Perhaps, the rationale ex pressed in HPF was best embodied by the ARSIS method (Ranchin, 1996), which gives excellent results when the scale ratio of the data is a power of 2 (Wald, 1997), but experiences some troubles otherwise (Blanc, 1998), because of non-flexible structure of the WT, which requires a filter-bank, i.e. a couple of filters, one low-pass and another high-pass, having complemen tary symmetry of responses, whose design is non-trivial. A different multi-resolution approach was pursued in the con text of Laplacian pyramids (LP) (Burt, 1983, 1984; Baronti, 1994; Aiazzi, 1997b) by other researchers, including the authors (Toet, 1989; Wilson, 1997; Aiazzi, 1997a, 1997c, 1998). Fol lowing the LP structure, an image is decomposed into nearly dis jointed band-pass channels in the spatial frequency domain, with out losing the spatial connectivity of its edges. The LP can be easily extended to deal with scales whose ratios are rational num bers, not only powers of two, as for the WT. Specifically, for a rational scale ratio (> 1) a unique low-pass filter is needed. Fur thermore, filter design is easy and non-critical for performances (Aiazzi, 1997a). Thus, data collected with ground resolutions of, say, 3 m, 5 m and 7 m can be easily compared and merged. When spatial enhancement was concerned, the GLP scheme has been shown to be superior in retaining multi-spectral sig natures (Aiazzi, 1998) also according to objective quality crite ria (Wald, 1997). However, fusion of two sets of multi-spectral data having different, and complementary, number of bands and ground resolution, may be also approached as a problem of spec tral enhancement of the higher-resolution data. In this paper, we wish to describe an example highlighting how the method is simple to be designed and generalized to images having a vari ety of ground resolutions, as future applications concerning new- generation sensors will demand in a close future.

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