Foreword to the Special Issue on Data Fusion

Gamba, Paolo; Chanussot, Jocelyn

doi:10.1109/tgrs.2008.919761

Cited by 20 publications

(12 citation statements)

References 65 publications

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“…Fusion of multi-sensor RS Data [218,219] RS data can be merged to integrate the advantages of high spatial, spectral and temporal resolution within one kind of sensor. Pan-sharpening processes to combine a spatially higher resolution pan-chromatic band with synchronously recorded lower resolution multi-spectral bands.…”

Section: Application Example Studies Main Findingsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances, resource limitations or unsustainable management, causing changes in forest health (FH) at various scales from the local to the global scale. Reactions to such stress depend on the phylogeny of forest species or communities and the characteristics of their impacting drivers and processes. There are many approaches to monitor indicators of FH using in-situ forest inventory and experimental studies, but they are generally limited to sample points or small areas, as well as being time-and labour-intensive. Long-term monitoring based on forest inventories provides valuable information about changes and trends of FH. However, abrupt short-term changes cannot sufficiently be assessed through in-situ forest inventories as they usually have repetition periods of multiple years. Furthermore, numerous FH indicators monitored in in-situ surveys are based on expert judgement. Remote sensing (RS) technologies offer means to monitor FH indicators in an effective, repetitive and comparative way. This paper reviews techniques that are currently used for monitoring, including close-range RS, airborne and satellite approaches. The implementation of optical, RADAR and LiDAR RS-techniques to assess spectral traits/spectral trait variations (ST/STV) is described in detail. We found that ST/STV can be used to record indicators of FH based on RS. Therefore, the ST/STV approach provides a framework to develop a standardized monitoring concept for FH indicators using RS techniques that is applicable to future monitoring programs. It is only through linking in-situ and RS approaches that we will be able to improve our understanding of the relationship between stressors, and the associated spectral responses in order to develop robust FH indicators.

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Section: Application Example Studies Main Findingsmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

et al. 2017

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“…feature level, and decision level) (Pohl & van Genderen 1998). The pixel level fusion is mainly applied to optical images, such as panchromatic and multispectral images (Pellemans et al 1993;Aiazzi et al 2007;Gangkofner et al 2008;Amolins et al 2007;Gamba & Chanussot 2008). The high level fusion includes multi-source data fusion, such as SAR, optical images, GIS data, LiDAR data, and in-situ survey data (Solberg et al, 1994;Zabuawala et al, 2009).…”

Section: Image and Data Fusion Technologiesmentioning

confidence: 99%

Data Fusion Technology of Multi-Platform Earth Observation on Agriculture

Xie

Xue

Zhai

et al. 2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Earth observation is the gathering of information via remote sensing technologies supplemented by earth surveying techniques, encompassing the collection, analysis and presentation of data. Remote sensing technology is playing a key role on precision agriculture. From the point of view of remote sensing and photogrammetry field, this article first took an overview of its applications on agriculture throughout past 3 decades, analyzed the advantages and disadvantages of different kinds of remote sensing when applied on agriculture respectively, and then discussed the solution on different platform based remote sensing image and data fusion. Finally, the paper summarizes potential trends tending to broaden the precision agriculture application of remote sensing data fusion.

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“…(Gamba and Chanussot, 2008) underline that co-registration remains a crucial step in numerous applications and still attracts a lot of attention. Co-registration is all the more important when sensors are different.…”

Section: Necessary Hypothesesmentioning

confidence: 99%

“…Defining this framework, (Wald, 1999), (Pohl and Van Genderen, 1998) and (Gamba and Chanussot, 2008) explain that there are three common levels for fusion. The lower level is the data level, concerning the observations of the sensors and related to physical measurements.…”

Section: Fusion Levelmentioning

confidence: 99%

“…Feature and decision-based fusion methods were explored for optical and radar fusion. For example, (Gamba and Chanussot, 2008) explain that feature extraction and combination at a geometrical level is considered a possible common practice in the near future to improve classification. The fact that fusion occurs on the the objects ensure that the approach is correct.…”

Section: Fusion Levelmentioning

confidence: 99%

See 1 more Smart Citation

A Proposed Framework to Unmix Scattering Mechanisms of Polarimetric Radar Images Using Very High Resolution Optical Images

Giordano

Mercier

Rudant³

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A new approach has been proposed for radar and optical image fusion. Candidate images for fusion are polarimetric radar images and very high resolution stereoscopic optical images. The fused product is expected to produce more reliable large scale land cover classifications. In the first part of this paper, observation-based state-of-the-art methods for optical and radar image fusion are discussed. The idea of unmixing physical radar scattering mechanisms with the optical images has been proposed as a suitable means to improve the resolution of the polarimetric radar images. The second part focuses on a general description of the target algorithm. The suggested physical approach simulating synthetic scattering matrices with stereoscopic optical images is developed. Finally, future work to develop this unmixing algorithm is discussed.

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Foreword to the Special Issue on Data Fusion

Cited by 20 publications

References 65 publications

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

Data Fusion Technology of Multi-Platform Earth Observation on Agriculture

A Proposed Framework to Unmix Scattering Mechanisms of Polarimetric Radar Images Using Very High Resolution Optical Images

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