Image Fusion Processing for IKONOS 1-m Color Imagery

Kalpoma, KaziA.; Kudoh, J.

doi:10.1109/tgrs.2007.897692

Cited by 59 publications

(21 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies report the problems and limitations associated with different fusion techniques (Chavez et al, 1991;Wald and Ranchin, 1997;Zhang, 2002). The most frequently encountered problem in fusion algorithms is that the fused image exhibits a notable deviation in visual appearance and spectral values from the original MS image (Ling et al, 2007;Kalpoma and Kudoh, 2007). Spectral distortions including spatial artifacts affect both manual and automated classifications because any error in the synthesis of the spectral signatures at the highest spatial resolution incurs an error in the decision (Ranchin et al, 2003).…”

Section: Image Fusion and Quality Assessmentmentioning

confidence: 99%

Benchmarking of data fusion algorithms in support of earth observation based Antarctic wildlife monitoring

Witharana

LaRue

Lynch

2016

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

a b s t r a c tRemote sensing is a rapidly developing tool for mapping the abundance and distribution of Antarctic wildlife. While both panchromatic and multispectral imagery have been used in this context, image fusion techniques have received little attention. We tasked seven widely-used fusion algorithms: Ehlers fusion, hyperspherical color space fusion, high-pass fusion, principal component analysis (PCA) fusion, University of New Brunswick fusion, and wavelet-PCA fusion to resolution enhance a series of single-date QuickBird-2 and Worldview-2 image scenes comprising penguin guano, seals, and vegetation. Fused images were assessed for spectral and spatial fidelity using a variety of quantitative quality indicators and visual inspection methods. Our visual evaluation elected the high-pass fusion algorithm and the University of New Brunswick fusion algorithm as best for manual wildlife detection while the quantitative assessment suggested the Gram-Schmidt fusion algorithm and the University of New Brunswick fusion algorithm as best for automated classification. The hyperspherical color space fusion algorithm exhibited mediocre results in terms of spectral and spatial fidelities. The PCA fusion algorithm showed spatial superiority at the expense of spectral inconsistencies. The Ehlers fusion algorithm and the wavelet-PCA algorithm showed the weakest performances. As remote sensing becomes a more routine method of surveying Antarctic wildlife, these benchmarks will provide guidance for image fusion and pave the way for more standardized products for specific types of wildlife surveys. Ó

show abstract

Section: Image Fusion and Quality Assessmentmentioning

confidence: 99%

Benchmarking of data fusion algorithms in support of earth observation based Antarctic wildlife monitoring

Witharana

LaRue

Lynch

2016

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

show abstract

“…One common assumption in some model-based and CS methods is that the Pan image X is a linear combination of HR MS images Z [54,[58][59][60][61][62][63][64][65][66] as in Equation (5). Equation (16) solves for these methods.…”

Section: Cs Methods From a Bayesian Perspectivementioning

confidence: 99%

A New Look at Image Fusion Methods from a Bayesian Perspective

Zhang

Huang

2015

Remote Sensing

View full text Add to dashboard Cite

Component substitution (CS) and multi-resolution analysis (MRA) are the two basic categories in the extended general image fusion (EGIF) framework for fusing panchromatic (Pan) and multispectral (MS) images. Despite of the method diversity, there are some unaddressed questions and contradictory conclusions about fusion. For example, is the spatial enhancement of CS methods better than MRA methods? Is spatial enhancement and spectral preservation competitive? How to achieve spectral consistency defined by Wald et al. in 1997? In their definition any synthetic image should be as identical as possible to the original image once degraded to its original resolution. To answer these questions, this research first finds out that all the CS and MRA methods can be derived from the Bayesian fusion method by adjusting a weight parameter to balance contributions from the spatial injection and spectral preservation models. The spectral preservation model assumes a Gaussian distribution of the desired high-resolution MS images, with the up-sampled low-resolution MS images comprising the mean value. The spatial injection model assumes a linear correlation between Pan and MS images. Thus the spatial enhancement depends on the weight parameter but is irrelevant of which category (i.e., MRA or CS) the method belongs to. This paper then adds a spectral consistency model in the Bayesian fusion framework to guarantee Wald's spectral consistency with OPEN ACCESSRemote Sens. 2015, 7 6829 regard to arbitrary sensor point spread function. Although the spectral preservation in the EGIF methods is competitive to spatial enhancement, the Wald's spectral consistency property is complementary with spatial enhancement. We conducted experiments on satellite images acquired by the QuickBird and WorldView-2 satellites to confirm our analysis, and found that the performance of the traditional EGIF methods improved significantly after adding the spectral consistency model.

show abstract

“…Information from multiple images covering the same scene can be fused at the feature level, but calibrating intensities of images acquired at different time points is difficult [19], [20], as different noise processes overlap [21]. It is computationally expensive [22], [23] and requires, ideally, some knowledge about the sensor [20]. Thus, a typical approach is to extract the information of interest in a first step, for example following a pattern classification approach, and then to fuse the information across observations in a second step, for example, by averaging the probabilistic maps or by assigning the vote of the majority of the observations [24], [25], [26], [27], [28].…”

Section: Prior Workmentioning

confidence: 99%

Multitemporal Fusion for the Detection of Static Spatial Patterns in Multispectral Satellite Images—With Application to Archaeological Survey

Menze¹,

Ur²

2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Abstract-We evaluate and further develop a multitemporal fusion strategy that we use to detect the location of ancient settlement sites in the Near East and to map their distribution, a spatial pattern that remains static over time. For each ASTER images that has been acquired in our survey area in north-eastern Syria, we use a pattern classification strategy to map locations with a multispectral signal similar to the one from (few) known archaeological sites nearby. We obtain maps indicating the presence of anthrosol -soils that formed in the location of ancient settlements and that have a distinct spectral pattern under certain environmental conditions -and find that pooling the probability maps from all available time points reduces the variance of the spatial anthrosol pattern significantly. Removing biased classification mapsi.e. those that rank last when comparing the probability maps with the (limited) ground truth we have -reduces the overall prediction error even further, and we estimate optimal weights for each image using a non-negative least squares regression strategy. The ranking and pooling strategy approach we propose in this study shows a significant improvement over the plain averaging of anthrosol probability maps that we used in an earlier attempt to map archaeological sites in a 20 000 km 2 area in northern Mesopotamia, and we expect it to work well in other surveying tasks that aim at mapping static surface patterns with limited ground truth in long series of multispectral images.

show abstract

Image Fusion Processing for IKONOS 1-m Color Imagery

Cited by 59 publications

References 26 publications

Benchmarking of data fusion algorithms in support of earth observation based Antarctic wildlife monitoring

Benchmarking of data fusion algorithms in support of earth observation based Antarctic wildlife monitoring

A New Look at Image Fusion Methods from a Bayesian Perspective

Multitemporal Fusion for the Detection of Static Spatial Patterns in Multispectral Satellite Images—With Application to Archaeological Survey

Contact Info

Product

Resources

About