IKONOS spatial resolution and image interpretability characterization

Ryan, Robert; Baldridge, Braxton; Schowengerdt, Robert A.; Choi, Taeyoung; Helder, Dennis L.; Blonski, Slawomir

doi:10.1016/j.rse.2003.07.006

Cited by 79 publications

(42 citation statements)

References 5 publications

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“…From the official product guide of QuickBird, we can find that its potential NIIRS is 4.9 [27]. The NIIRS of IKONOS is estimated to be 4.5, which can be found in the literature of Ryan [14]. has the same problem as ZY-3 in calculating NIIRS using GIQE, in that their GSD (2.5 m) exceed the scope of that in GIQE 4.0 (2.03 m).…”

Section: Niirs Level Of the Zy-3 Datamentioning

confidence: 99%

“…Currently, the research on calculating image interpretability using the GIQE is concentrated on satellite data with very high resolution (VHR), such as QuickBird, IKONOS and Komspat-2 [6,[14][15][16]. Ryan et al [14] discussed the relationship between the NIIRS value and the other parameters of IKONOS imagery.…”

Section: Introductionmentioning

confidence: 99%

“…Ryan et al [14] discussed the relationship between the NIIRS value and the other parameters of IKONOS imagery. Kim et al [6] predicted the NIIRS value of IKONOS and QuickBird satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data

Li¹,

Luo²,

Zhu³

2014

Remote Sensing

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Image quality is important for taking full advantage of satellite data. As a common indicator, the National Imagery Interpretability Scale (NIIRS) is widely used for image quality assessment and provides a comprehensive representation of image quality from the perspective of interpretability. The ZY-3 (Ziyuan-3) satellite is the first civil high resolution mapping satellite in China, which was established in 2012. So far, there has been no reports on adopting NIIRS as the common indicator for the quality assessment of that satellite image data. This lack of a common quality indicator results in a gap between satellite data users around the world and those in China regarding the understanding of the quality and usability of ZY-3 data. To overcome the gap, using the general image-quality equation (GIQE), this study evaluates the ZY-3 sensor-corrected (SC) panchromatic nadir (NAD) data in terms of the NIIRS. In order to solve the uncertainty resulting from the exceeding of the ground sample distance (GSD) of ZY-3 data (2.1 m) in GIQE (less than 2.03 m), eight images are used to establish the relationship between the manually obtained NIIRS and the GIQE predicted NIIRS. An adjusted GIQE is based on the relationship and verified by another five images. Our study demonstrates that the method of using adjusted GIQE for calculating NIIRS can be used for the quality assessment of ZY-3 satellite images and reveals that the NIIRS value of ZY-3 SC NAD data is about 2.79.

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Section: Niirs Level Of the Zy-3 Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data

Li¹,

Luo²,

Zhu³

2014

Remote Sensing

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“…In the image quality assessment and estimation, the U.S. government's IRARS and its collaborators jointly developed the NIIRS standard [3,4]. The GIQE equation provides a forecast for the NIIRS grade [5,6], performance prediction of the remote sensing system can be given in remote sensing system designing and parameters, the quality assessment of remote sensing image can be given before system development [7], so as to optimize the design of remote sensor. In the process of Pleiades system demonstration, France has made a comprehensive balance of several important parameters (MTF, SNR, compression ratio, deconvolution, de noise, etc.)…”

Section: Introductionmentioning

confidence: 99%

Optimization Design Method of Optical Remote Sensor Based on Imaging Chain Simulation

Xing¹,

Cao²,

Yue³

et al. 2017

MATEC Web Conf.

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Abstract. Space optical remote sensor of high spatial resolution need achieve both light and small design and high imaging quality. An optimization method of optical remote sensor based on imaging chain simulation is presented. All factors in imaging chain are analyzed according to MTF models and noise models. Additionally, image restoration is considered as one part of imaging chain. The simulation model are built to optimize remote sensor overall performance by the evaluation of remote sensor and simulation image. This method is used to enhance image quality and reduce the development difficulties of optical remote sensor The test validates the feasibility and predominance of the optimization design method

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“…For a high-resolution on-orbit sensor, the blur can be derived from the degraded image or the scene itself by measuring man-made targets or typical features [6]. A great deal of effort has been made to improve the MTF measurement methods, which include the knife-edge method [7], the trap-based target method [7], and the pulse method [8]. Once the MTF is obtained, the remote sensing images can be restored by applying an MTF compensation (MTFC) technique [2,9] or other non-blind restoration methods [5,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

et al. 2014

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Abstract:In this paper, a blind restoration method is presented to remove the blur in remote sensing images. An alternating minimization (AM) framework is employed to simultaneously recover the image and the point spread function (PSF), and an adaptive-norm prior is used to apply different constraints to smooth regions and edges. Moreover, with the use of the knife-edge features in remote sensing images, an automatic knife-edge detection method is used to obtain a good initial PSF for the AM framework. In addition, a no-reference (NR) sharpness index is used to stop the iterations of the AM framework automatically at the best visual quality. Results in both simulated and real data experiments indicate that the proposed AM-KEdge method, which combines the automatic knife-edge detection and the AM framework, is robust, converges quickly, and can stop automatically to obtain satisfactory results.

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IKONOS spatial resolution and image interpretability characterization

Cited by 79 publications

References 5 publications

Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data

Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data

Optimization Design Method of Optical Remote Sensor Based on Imaging Chain Simulation

Blind Restoration of Remote Sensing Images by a Combination of Automatic Knife-Edge Detection and Alternating Minimization

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