&lt;title&gt;IKONOS technical performance assessment&lt;/title&gt;

Cook, Mark K.; Peterson, Brad A; Dial, Gene; Gibson, Laurie; Gerlach, Frank W.; Hutchins, Kevin S.; Kudola, Robert; Bowen, Howard S.

doi:10.1117/12.436997

Cited by 35 publications

(21 citation statements)

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“…To evaluate our proposed pan-sharpening method, we use imagery from the IKONOS multispectral imaging satellite [29], for which details of the system specifications, including the spectral responsivities and point-spread function characteristics are publicly available [21] along with a number of image datasets [30] comprised of coarsely registered pan and multispectral images at their native capture resolutions. Together these data provide an ideal test set for evaluating our proposed algorithm and for benchmarking its performance against previously proposed pan-sharpening alternatives.…”

Section: Resultsmentioning

confidence: 99%

“…Together these data provide an ideal test set for evaluating our proposed algorithm and for benchmarking its performance against previously proposed pan-sharpening alternatives. The IKONOS satellite has five imaging channels: a panchromatic channel p[x] with a nominal ground resolution of X = 1m along each dimension and spanning the spectral range 5 from 525.8 through 928.5 nm and K = 4 multispectral bands with a nominal ground resolution of 4X = 4m along each dimension (i.e., q = 4) and having spectral bandwidths for the channels as follows [21] (Fig. S.1).…”

Section: Resultsmentioning

confidence: 99%

“…Note that unlike some other imaging scenarios, the noise level usually varies quite significantly across the different spectral bands because of the significant differences in the noise sources and in the native sensitivity of the underlying sensor used to capture the images. As a result, differences as high as 3-dB are not uncommon for the SNR for the different spectral bands [21].…”

Section: Proposed Formulation Of Pan-sharpening As An Optimizatiomentioning

confidence: 99%

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A Regularized Model-Based Optimization Framework for Pan-Sharpening

Aly

Sharma

2014

IEEE Trans. on Image Process.

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Abstract-Pan-sharpening is a common postprocessing operation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. In this paper, pan-sharpening is formulated as the problem of jointly estimating the high-resolution (HR) multispectral images to minimize an objective function comprised of the sum of squared residual errors in physically motivated observation models of the low-resolution (LR) multispectral and the HR panchromatic images and a correlation-dependent regularization term. The objective function differs from and improves upon previously reported model-based optimization approaches to pan-sharpening in two major aspects: 1) a new regularization term is introduced and 2) a highpass filter, complementary to the lowpass filter for the LR spectral observations, is introduced for the residual error corresponding to the panchromatic observation model. To obtain pan-sharpened images, an iterative algorithm is developed to solve the proposed joint minimization. The proposed algorithm is compared with previously proposed methods both visually and using established quantitative measures of SNR, spectral angle mapper, relative dimensionless global error in synthesis, Q, and Q4 indices. Both the quantitative results and visual evaluation demonstrate that the proposed joint formulation provides superior results compared with pre-existing methods. A software implementation is provided.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Proposed Formulation Of Pan-sharpening As An Optimizatiomentioning

confidence: 99%

See 1 more Smart Citation

A Regularized Model-Based Optimization Framework for Pan-Sharpening

Aly

Sharma

2014

IEEE Trans. on Image Process.

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“…To evaluate our proposed pan-sharpening method, we use imagery from the IKONOS multispectral imaging satellite [24], for which details of the system specifications, including the spectral responsivities and point-spread function characteristics are publicly available [25] along with a limited number of image datasets comprised of registered pan and multispectral images at their native capture resolutions. Together these data provide an ideal test set for evaluating our proposed algorithm and for benchmarking its performance against previously proposed pan-sharpening alternatives.…”

Section: Resultsmentioning

confidence: 99%

Joint multichannel pansharpening for multispectral imagery

Aly

Sharma

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Pan-sharpening is a common post-processing operation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. Previously proposed pan-sharpening techniques operate on a per-channel basis, sharpening each multispectral band independently based on the panchromatic image, often in an ad hoc manner. In contrast with most prior techniques, we formulate pan-sharpening as the problem of jointly estimating the high resolution multispectral images to minimize the combined squared residual error in physically motivated observation models of the low resolution multispectral and the high resolution panchromatic images. To realize pan-sharpening using our proposed formulation, we develop an iterative algorithm to solve the joint minimization resulting in an overall algorithm with modest computational complexity. We evaluate our proposed algorithm and benchmark it against previously proposed methods using established quantitative measures of SNR, SAM, ERGAS, Q, and Q4 indices. Both the quantitative results and visual evaluation demonstrate that the proposed joint formulation provides superior results compared with pre-existing methods.Index Terms-pan-sharpening, satellite imagery, image fusion, spectral imaging INTRODUCTIONFor reasons of cost, bandwidth, and to maintain adequate image quality in the presence of noise, satellite based multi and hyper spectral image capture systems use on-board imaging sensors that vary in spatial resolution: typical sensor configurations, capture a high resolution panchromatic image spanning a wide spectral band and lower resolution images for individual spectral bands. For applications using the satellite imagery, once data is received on the ground, the spectral band images are post-processed to obtain versions that match the higher resolution sampling of the panchromatic image. This process, commonly referred to as pan-sharpening, merges together low resolution and spectral information captured in spectral channels with high resolution detail from the panchromatic image. Several image pan-sharpening methods have been proposed in the literature. A majority of these techniques operate using a component or subband substitution framework where the multispectral and the panchromatic images are mapped into a transform domain and in the transform domain, some component or subband data of the panchromatic image is inserted or used to replace the data in the multispectral image, and the inverse transform is then applied to this modified transform domain spectral image to obtain the pan-sharpened version. Common examples in the substitution framework include: a) methods based purely on "color", i.e. channel transforms, such as intensity-hue-saturation (IHS) substitution [1][2][3], Brovey transform, principal component replacement, Gram-Schmidt transform [4], and b) methods based on spatial transforms, such as the multi-scale wavelet decomposition...

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“…The IKonos-2 satellite can reach a positioning accuracy of 15 m without GCPs [49]. The WorldView-2 satellite can reach a positioning accuracy of 6.5 m without GCPs [50].…”

Section: Proposed Self-calibration Methodsmentioning

confidence: 99%

Self-Calibration Method Based on Surface Micromaching of Light Transceiver Focal Plane for Optical Camera

Zhang

Liu

et al. 2016

Remote Sensing

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Abstract:In remote sensing photogrammetric applications, inner orientation parameter (IOP) calibration of remote sensing camera is a prerequisite for determining image position. However, achieving such a calibration without temporal and spatial limitations remains a crucial but unresolved issue to date. The accuracy of IOP calibration methods of a remote sensing camera determines the performance of image positioning. In this paper, we propose a high-accuracy self-calibration method without temporal and spatial limitations for remote sensing cameras. Our method is based on an auto-collimating dichroic filter combined with a surface micromachining (SM) point-source focal plane. The proposed method can autonomously complete IOP calibration without the need of outside reference targets. The SM procedure is used to manufacture a light transceiver focal plane, which integrates with point sources, a splitter, and a complementary metal oxide semiconductor sensor. A dichroic filter is used to fabricate an auto-collimation light reflection element. The dichroic filter, splitter, and SM point-source focal plane are integrated into a camera to perform an integrated self-calibration. Experimental measurements confirm the effectiveness and convenience of the proposed method. Moreover, the method can achieve micrometer-level precision and can satisfactorily complete real-time calibration without temporal or spatial limitations.

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<title>IKONOS technical performance assessment</title>

Cited by 35 publications

References 0 publications

A Regularized Model-Based Optimization Framework for Pan-Sharpening

A Regularized Model-Based Optimization Framework for Pan-Sharpening

Joint multichannel pansharpening for multispectral imagery

Self-Calibration Method Based on Surface Micromaching of Light Transceiver Focal Plane for Optical Camera

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