Review article Multisensor image fusion in remote sensing: Concepts, methods and applications

Pohl, Christine; Genderen, J.L. van

doi:10.1080/014311698215748

Cited by 1,980 publications

(1,089 citation statements)

References 54 publications

(71 reference statements)

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“…For the full exploitation of increasingly sophisticated multisource data, advanced analytical or numerical data fusion techniques are being developed [1,2]. Multisource data fusion is probably the most difficult aspect in the integration of remote sensing image data products.…”

Section: Introductionmentioning

confidence: 99%

“…A good example of this is the fusion of images acquired by different sensors having a different spatial resolution and of different spectral resolution. [1] states that the fusion of these disparate data contributes to the increasing land cover enhancement.…”

Section: Introductionmentioning

confidence: 99%

“…A detailed review in the literature is given by [1]. In which some methods, like intensity-hue-saturation (IHS) [11,12], Brovey transform [13,14], and principal component analysis [14,15], provide superior visual highresolution multispectral fused images, but have a limitation of the need of high-quality spectral information as input, while these methods are useful for visual interpretation.…”

Section: Introductionmentioning

confidence: 99%

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Quality Assessment of Fused Image of Modis and Palsar

Kumar¹,

Singh

2010

PIER B

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Abstract-It is a current need of research to extensively use the freely available satellite images. The most commonly available satellite images are Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). The problems with these images are their poor spatial resolution that restricts their use in various applications. This restriction may be minimized by application of the fusion techniques where high resolution image will be used to fuse with low resolution images. Another important aspect of fusion of different sensors data as optical and radar images (where both can provide the complimentary information), and the resultant fused image after fusion may give enhanced and useful information that may be beneficial for various applications. Therefore, in this paper an attempt has been made to fuse the full polarimetric Phased Array type L-band SAR (PALSAR) image with MODIS image and assess the quality of fused image. PALSAR image has a advantage of availability of data in four different channels. These four channels are HH (Transmitted horizontal polarization and received also in horizontal polarization), HV (Transmitted horizontal polarization and received vertical polarization), VH (Transmitted vertical polarization and received horizontal polarization) and VV (Transmitted vertical polarization and received vertical polarization), which provides various landcover information. The curvelet based fusion technique has been applied to MODIS Bands 1 and 2 and PALSAR (HH, HV and VV) bands for assessing the effect of fusion in land cover distinction. The three major land covers i.e., agriculture, urban and water are considered for evaluation of fusion of these images for the Roorkee area of India. The results are quite encouraging, and in near future 192Harish Kumar and Singh it may provide a better platform for the maximize the use of MODIS images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quality Assessment of Fused Image of Modis and Palsar

Kumar¹,

Singh

2010

PIER B

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“…The Brovey Transform attempts to normalize digital number DN values of the multispectral bands and then multiplies the result by the DN of the high spatial resolution data to add the intensity or brightness component of the image [9]. In case of Brovey transformation three input bands were red and infrared from optical data of May 29 and SAR HH polarization backscatter from May 29.…”

Section: Brovey Transformmentioning

confidence: 99%

“…Proper geometric registration is essential to avoid artifacts in the merged image [9]. Twenty control points were selected between Radarsat2 and LISS III data, considering Radarsat2 as the master image.…”

Section: Fusion Of Optical and Sar Datamentioning

confidence: 99%

Jute and Tea Discrimination Through Fusion of Sar and Optical Data

Haldar¹,

Patnaik²,

Mohan³

et al. 2012

PIER B

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Abstract-Remote sensing approaches based on both optical and microwave region of EM spectra have been widely adapted for large scale crop monitoring and condition assessment. Visible, infrared and microwave wavelengths are sensitive to different crop characteristics, thus data from optical and radar sensors are complementary. Synthetic Aperture Radar (SAR) responds to the large scale crop structure (size, shape and orientation of leaves, stalks, and fruits) and the dielectric properties of the crop canopy. Research is needed to assess the saturation effects of SAR data and to investigate the synergy between the optical and SAR imagery for exploring various dimensions of crop growth which is not possible with any one of them singly with higher degree of accuracy. An attempt has been made to study the potential of SAR and optical data individually and by fusing them to separate various landcover classes. Two-date and three-date SAR data could distinguish jute and tea crop with 70-85% accuracy, while cloud free optical data (green, red and infrared bands) resulted in accuracy 80-85%. On fusing the optical and SAR single date data of May, 29 2010 using Brovey method, an accuracy of 85% was obtained. PCA and HSV with munsell based approaches resulted in similar accuracies but HSV performed the best among these. This emphasizes on the synergistic effect of SAR and optical data. Also the fused data could be used to delineate the crop condition and age by inputs like NDVI from optical and XPR (Cross polarization ratio) from SAR data.The co-and cross polarization ratios along with various indices viz. Biomass Index (BMI), Volume Scattering Index (VSI) and canopy structural index (CSI) were used to discriminate tea from jute. Due to differences in structural component of tea and jute at early season as manifested by the indices, there is clear separability as observed from the mean values. Among the dual polarization combinations, HV/VV performed the best (70%) followed by HV/HH (62%) and lastly HH/VV (42%). Among the single best indices for discrimination BMI performed the best. Combination of Co, Cross-polarization and BMI yields around 80% classification accuracy. BMI and VSI combination yielded the best classification accuracy of 84%. This level of accuracy obtained was much superior to that of multidate HH polarization SAR data.

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Land Cover Assessment and Monitoring

Lambin

2000

Encyclopedia of Analytical Chemistry

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Land‐use changes are cumulatively transforming land cover at an accelerating pace, mainly in the tropics. These changes in terrestrial ecosystems are closely linked with the issue of the sustainability of socioeconomic development since they affect essential parts of our natural capital such as climate, soils, vegetation, water resources and biodiversity. Changes in terrestrial ecosystems brought about by human activity are driven by land cover conversion, land degradation, land‐use intensification or other forms of land cover modification. The term land cover refers to the attributes of a part of the Earth's land surface and immediate subsurface, including biota, soil, topography, surface and groundwater, and human structures. The term land use refers to the purposes for which humans exploit the land cover. One generally distinguishes between land cover conversion, the complete replacement of one cover type by another, and land cover modification, more subtle changes that affect the character of the land cover without changing its overall classification. Land cover modifications are generally more prevalent than land cover conversions. Two types of satellite sensors are used in land cover applications: fine spatial resolution systems and coarse spatial resolution/high temporal frequency systems. Data are collected in the visible, thermal and microwave ranges of the spectrum. Information in the spatial domain also provides valuable information on land surface attributes. These data are analyzed either for land cover mapping or for land cover monitoring. Current research is moving toward the direct estimation of biophysical surface parameters by model inversion methods. Multisensor approaches are also increasingly being developed.

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Review article Multisensor image fusion in remote sensing: Concepts, methods and applications

Cited by 1,980 publications

References 54 publications

Quality Assessment of Fused Image of Modis and Palsar

Quality Assessment of Fused Image of Modis and Palsar

Jute and Tea Discrimination Through Fusion of Sar and Optical Data

Land Cover Assessment and Monitoring

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