A review of data fusion models and systems

Sidek, Othman; Quadri, S. A.

doi:10.1080/19479832.2011.645888

Cited by 42 publications

(14 citation statements)

References 27 publications

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“…Height error is pointed, lines, or areas that have high value, but that value does not represent the situation in the field (Julzarika, 2011a). ERS-Tandem (Knopfle, Strunz, and Roth, 1998) Merging height model using two methods, namely integration and fusion (Sidek and Quadri, 2012). The concept of integration and fusion refers to the concept developed by previous studies (Hoja and d'Angelo, 2010;Hoja et al, 2006).…”

Section: Materials and Methodologymentioning

confidence: 99%

Height Model Integration Using Alos Palsar, X Sar, SRTM C, and Icesat/Glas

Julzarika

2017

IJReSES

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Abstract. The scarcity of height models is one of the important issues in Indonesia. ALOS PALSAR, X SAR, SRTM C, and ICESAT/GLAS are free available global height models. Four data can be integrated the height models. Integration takes advantage of each characteristic data. The spatial resolution uses ALOS PALSAR. ICESAT/GLAS has a minimal height error because it is DTM. SAR has advantages of minimal error in the highland and need a low pass filter on the lowland. DSM uses X SAR and DEM from ALOS PALSAR. Characteristics and penetration of vegetation objects can be seen from the wavelength type of SAR data. This research aims to make height model integration in order to get the vertical accuracy better than vertical accuracy of global height models and minimum height error. The study area is located in Karo Regency. The first process is to crop the height models into Karo Regency, geoid undulation correction using EGM 2008. The next step is to detect pits and spires by using radius value 1000 m and depth +1.96σ (+5 m) with uncertainty 95,45%. Then generate HEM and height model integration. To know the accuracy of this height model, 100 reference points measured using GNSS, altimeter, and similar point observed on the height model integration are selected. The accuracy test covers RMSE, accuracy (z), and height difference test. The result of this study shows that the height model integration has a vertical accuracy in 1.14 m. This height model integration can be used for mapping scale 1: 10.0000. Height models can be created from optical, radar, microwave, lidar, and sonar data (Bhardwaj et al., 2013): (Mitchell and MacNabb, 2010). Height model of the optical data using optical satellite imagery data, aerial photographs, video (Deilami and Hashim, 2011). In the optical data, height model using stereo models (Saldana et al., 2012) height model on the radar data using stereo models, interferometry, and depth cue perceptive. Height models are made by sonar can use data Interferometry Synhetic Aperture Sonar (IFSAS) data (Julzarika, 2011b). Height models or also known as 3D model is a view of a model with the 3D coordinate system (polar, geodetic, raster and cartesian) with field-defined reference to a specific projection and datum (Julzarika and Sudarsono, 2009). A height model can be created from radar and optical data. Height models can also be defined as a digital model that gives information about the earth surface shape (topography) in the form of raster or vector data (Freeden et al., 2010). Height models consist of two information, namely: height data and the data of such height position coordinates on the earth's surface (Honikel, 1998).

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Section: Materials and Methodologymentioning

confidence: 99%

Height Model Integration Using Alos Palsar, X Sar, SRTM C, and Icesat/Glas

Julzarika

2017

IJReSES

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“…Since the 1980s, studies on pansharpening with multiple source images for better understanding land surface feature has been conducted [48,49]. Many methods have been proposed to realize the fusion process and they can be classified into two categories: component substitution CS-based and multiresolution analysis MRA-based techniques [50].…”

Section: Pansharpening Algorithmsmentioning

confidence: 99%

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Liu

Wang

Ling

et al. 2017

Water

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Abstract:Coastline extraction is a fundamental work for coastal resource management, coastal environmental protection and coastal sustainable development. Due to the free access and long-term record, Landsat series images have the potential to be used for coastline extraction. However, dynamic features of different types of coastlines (e.g., rocky, sandy, artificial), caused by sea level fluctuation from tidal, storm and reclamation, make it difficult to be accurately extracted with coarse spatial resolution, e.g., 30 m, of Landsat images. To access this problem, we analyze the performance of coastline extraction by integrating downscaling, pansharpening and water index approaches in increasing the accuracy of coastline extraction from the latest Landsat-8 Operational Land Imager (OLI) imagery. In order to prove the availability of the proposed method, we designed three strategies: (1) Strategy 1 uses the traditional water index method to extract coastline directly from original 30 m Landsat-8 OLI multispectral (MS) image; (2) Strategy 2 extracts coastlines from 15 m fused MS images generated by integrating 15 m panchromatic (PAN) band and 30 m MS image with ten pansharpening algorithms; (3) Strategy 3 first downscales the PAN band to a finer spatial resolution (e.g., 7.5 m) band, and then extracts coastlines from pansharpened MS images generated by integrating downscaled spatial resolution PAN band and 30 m MS image with ten pansharpening algorithms. Using the coastline extracted from ZiYuan-3 (ZY-3) 5.8 m MS image as reference, accuracies of coastlines extracted from MS images in three strategies were validated visually and quantitatively. The results show that, compared with coastline extracted directly from 30 m Landsat-8 MS image (strategy 1), strategy 3 achieves the best accuracies with optimal mean net shoreline movement (MNSM) of −2.54 m and optimal mean absolute difference (MAD) of 11.26 m, followed by coastlines extracted in strategy 2 with optimal MNSM of −4.23 m and optimal MAD of 13.54 m. Further comparisons with single-band thresholding (Band 6), AWEI, and ISODATA also confirmed the superiority of strategy 3. For the various used pansharpening algorithms, five multiresolution analysis MRA-based pansharpening algorithms are more efficient than the component substitution CS-based pansharpening algorithms for coastline extraction from Landsat-8 OLI imagery. Among the five MRA-based fusion methods, the coastlines extracted from the fused images generated by Indusion, additive à trous wavelet transform (ATWT) and additive wavelet luminance proportional (AWLP) produced the most accurate and visually realistic representation. Therefore, pansharpening approaches can improve the accuracy of coastline extraction from Landsat-8 OLI imagery, and downscaling the PAN band to finer spatial resolution is able to further improve the coastline extraction accuracy, especially in crenulated coasts.

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“…A number of pan-sharpening methods have been developed. There are several good review papers on this topic, including Zhang (2010b), Sidek and Quadri (2012), Yang and Zhang (2014) and Pohl and Van Genderen (1998. Most pansharpening methods alter the spectral properties of MS images while improving the spatial resolution (Alparone et al 2004, Ehlers et al 2010, and thus, one major issue when developing pan-sharpening methods is to improve the spatial resolution while retaining the spectral consistency.…”

Section: Introductionmentioning

confidence: 99%

Effects of the spectral properties of a panchromatic image on pan-sharpening simulated using hyperspectral data

Matsuoka

Tadono

Yoshioka

2016

International Journal of Image and Data Fusion

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This study proposes a methodology for simulating pan-sharpening using hyperspectral (HS) data to evaluate the influence of the spectral properties (band position and band width) of panchromatic (PAN) data on the quality of pan-sharpened data. PAN images were generated from Airborne Visible/Infrared Imaging Spectrometer HS data by changing the position and width of the spectral waveband, and these images were subjected to pan-sharpening with multispectral (MS) bands, also generated from HS data. The qualities of pansharpened MS data were evaluated using five measures: erreur relative globale adimensionnelle de synthèse, weighted signal-to-noise ratio, universal image quality index, quality index based on local variance and objective image fusion performance measure. This methodology was applied to three pan-sharpening methods (Gram-Schmidt spectral sharpening, block-based synthetic variable ratio and generalised Laplacian pyramid with spectral distortion minimisation) to characterise the spectral and spatial qualities of pan-sharpened images. The results show that the pan-sharpened images were affected by the spectral properties of the PAN data to varying degrees depending on the pan-sharpening method used and the land cover type. Better-quality images were obtained if the spectral properties of the PAN and MS bands were similar. No one spectral property was identified as best for all MS bands. This methodology will be helpful for the evaluation of the spectral behaviour of pan-sharpening methods, the development of new pan-sharpening methods and the design of new sensors suitable for pan-sharpening. ARTICLE HISTORY

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A review of data fusion models and systems

Cited by 42 publications

References 27 publications

Height Model Integration Using Alos Palsar, X Sar, SRTM C, and Icesat/Glas

Height Model Integration Using Alos Palsar, X Sar, SRTM C, and Icesat/Glas

Analysis of Coastline Extraction from Landsat-8 OLI Imagery

Effects of the spectral properties of a panchromatic image on pan-sharpening simulated using hyperspectral data

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