General Image-Quality Equation: GIQE

Leachtenauer, Jon C.; Malila, W. A.; Irvine, John; Colburn, Linda; Salvaggio, Nanette

doi:10.1364/ao.36.008322

Cited by 214 publications

(166 citation statements)

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“…With the NIIRS criteria published by IRARS, the NIIRS can be estimated by human operators. The results of operators on one image are so close, with a standard deviation of 0.3 NIIRS [12], that this observed NIIRS (ONIIRS) value can be considered as the approximate value of the true NIIRS value for ZY-3 image interpretability. Meanwhile, the GIQE can be used to calculate the predicted/estimated NIIRS (PNIIRS) value for the same image.…”

Section: Introductionmentioning

confidence: 65%

“…From the perspective of the users, the NIIRS is probably the best measure of assessing the quality of panchromatic images in terms of interpretability [11]. Higher interpretability indicates that more ground objects can be detected or identified, and interpretability in an intuitive and quantitative sense determines which task can be performed with the imagery [12].…”

Section: Introductionmentioning

confidence: 99%

“…According to the study of Leachtenauer et al [12], the revised GIQE is validated over the conditioned range listed in Table 2. The accuracy and validity of the GIQE outside of these bounds is unknown.…”

Section: Introductionmentioning

confidence: 99%

“…The relationship can be found by a regression analysis using the ONIIRS and the PNIIRS as variables. If there is an acceptable correlation between the ONIIRS and PNIIRS, it would be possible to use this regression expression to estimate the true NIIRS for a ZY-3 image, i.e., the adjusted GIQE can be still used for the estimation of the interpretability of the ZY-3 images, even if its GSD is out of the range designated by IRARS [12].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Introductionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Imaging Science in Overhead Surveillance

Fiete

2002

Encyclopedia of Imaging Science and Technology

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Overhead surveillance has come a long way since Tournachon (“Nadar”) collected the first aerial photographs from a hot air balloon in 1858. The invention of the plane made overhead surveillance more practical. Images from space began with Explorer IV in 1959. Today an increasing amount of overhead surveillance imagery is collected by systems that do not require pilots. Satellites are used to collect data high above the earth. (UAVs) (unmanned aerial vehicles) are being used to collect information at lower altitudes. Overhead surveillance systems continue to provide environmental and meteorological data to the scientific community as well as important intelligence data from areas that are difficult to access. Linking together many steps in an image chain creates the final image product produced by an overhead surveillance system. Each link plays a vital role in the final quality of the image. This article discusses the various steps. Surveillance systems are generally categorized by the wavelength region of the electromagnetic spectrum that they image. The earliest cameras used black and white film that captured panchromatic images, i.e., film that is sensitive to the visible portion of the electromagnetic spectrum. Even with CCD (charged coupled devices) detectors today, black and white images are the most common because the light is captured over a broad spectrum of wavelengths, thus allowing a high enough signal to collect high‐resolution images. Visible imaging systems generally acquire images using the sun as the illumination source. After the image data has been downlinked to the groundstation and decompressed, it may be hard to interpret the image due to low contrast, blurred edges, streaks from sensitivity differences between detectors, failed detectors, and discontinuities at the segment boundaries. Image processing techniques can be used to optimize the visual interpretability of the image data. Calibration is performed to remove the streaks, and the data from each detector segment are processed to synthesize an image collected with a single continuous detector array. The image is also processed to remove any image motion effects, such as vehicle oscillation, and geometric distortions that might occur. The image data can also be processed to a specific mapping spatial resolution. The utility of an image should not be equated with quality of the image. For example, geographic surveys can be performed better with overhead images that trade‐off lower resolution for a larger area of coverage. The image acquired at the lower altitude has more detail that can be resolved on the ground, but much of the surrounding information is lost, which may be very important to understand the context of the objects on the ground.

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General Image-Quality Equation: GIQE

Cited by 214 publications

References 1 publication

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

Imaging Science in Overhead Surveillance

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