National Imagery Interpretability Rating Scale Research Articles

Evaluation of jitter vibration-induced image interpretability degradation for remote sensing data using GIQE

Remote sensing data plays an important role in various military and civilian applications. The quality of remote sensing data is the key to the success of these applications. remote sensing data are subjected to a variety of distortions from different sources, including acquisition, onboard compression, transmission, on-ground processing, and satellite vibrations. These distortions cause a great loss of image quality. In this paper, jitter vibration is modeled according to the modulation transfer function (MTF). Jitter-induced degradation is evaluated in terms of the National Imagery Interpretability Rating Scale (NIIRS), which is measured in terms of the general image quality equation (GIQE). A study on the third, fourth, and fifth versions of the GIQE is made using a remote sensing data set to select the appropriate version that could be used in evaluating the amount of jitter-induced degradation. The key result of this paper is that the fourth version of the GIQE is the most proper one in evaluating the amount of jitter-induced degradation.

A Remote Sensing Image Quality Interpretation Scale Characterization Method Based on the TTP Criterion

Accurate grading of remote sensing image interpretation is crucial for improving image classification and screening efficiency. Through extensive research, the General Image Quality Equation (GIQE) based on the National Imagery Interpretability Rating Scale (NIIRS) has been developed. However, poor reliability and low accuracy issues remain due to the failure to consider human visual characteristics. This paper introduces the Target Task Performance (TTP) criterion as a key parameter to reflect the cascading degradation factors of human visual perception characteristics and system imaging links, which improves the reliability of the model. A New Optimized Remote Sensing Image Quality Equation (NORSIQE), which effectively predicted the interpretability of image information, is constructed. Using 200 sets of test data, the quantitative relationship between key parameters (GSD, TTP, and SNR) of the NORSIQE and the subjective NIIRS level is obtained by a least squares regression fit, and the determination coefficient of the model is as high as 0.916. The model is evaluated for accuracy using 120 sets of validation data, showing an 87% improvement compared to the GIQE4. This method provides theoretical support for the development of new methods for remote sensing image quality evaluation and the design of payloads for remote sensing imaging systems.

Airborne validation of the general image quality equation 5.

The general image quality equation (GIQE) maps imaging system parameters to performance in terms of common detection and recognition tasks. Changes to the National Imagery Interpretation Rating Scale led to the development of GIQE version 5 in 2015. The purpose of this paper is to provide a review of GIQE 5 applications in the literature, a tutorial on its use, and, for the first time , an independent validation of the new model along with comparisons to GIQEs 3 and 4.

Image Interpretability of nSight-1 Nanosatellite Imagery for Remote Sensing Applications

Nanosatellites are increasingly being used in space-related applications to demonstrate and test scientific capability and engineering ingenuity of space-borne instruments and for educational purposes due to their favourable low manufacturing costs, cheaper launch costs, and short development time. The use of CubeSat to demonstrate earth imaging capability has also grown in the last two decades. In 2017, a South African company known as Space Commercial Services launched a low-orbit nanosatellite named nSight-1. The demonstration nanosatellite has three payloads that include a modular designed SCS Gecko imaging payload, FIPEX atmospheric science instrument developed by the University of Dresden and a Radiation mitigation VHDL coding experiment supplied by Nelson Mandela University. The Gecko imager has a swath width of 64 km and captures 30 m spatial resolution images using the red, green, and blue (RGB) spectral bands. The objective of this study was to assess the interpretability of nSight-1 in the spatial dimension using Landsat 8 as a reference and to recommend potential earth observation applications for the mission. A blind image spatial quality evaluator known as Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) was used to compute the image quality for nSight-1 and Landsat 8 imagery in the spatial domain and the National Imagery Interpretability Rating Scale (NIIRS) method to quantify the interpretability of the images. A visual interpretation was used to propose some potential applications for the nSight1 images. The results indicate that Landsat 8 OLI images had significantly higher image quality scores and NIIRS results compared to nSight-1. Landsat 8 has a mean of 19.299 for the image quality score while nSight-1 achieved a mean of 25.873. Landsat 8 had NIIRS mean of 2.345 while nSight-1 had a mean of 1.622. The superior image quality and image interpretability of Landsat could be attributed for the mature optical design on the Landsat 8 satellite that is aimed for operational purposes. Landsat 8 has a GDS of 30-m compared to 32-m on nSight-1. The image degradation resulting from the lossy compression implemented on nSight-1 from 12-bit to 8-bit also has a negative impact on image visual quality and interpretability. Whereas it is evident that Landsat 8 has the better visual quality and NIIRS scores, the results also showed that nSight-1 are still very good if one considers that the categorical ratings consider that images to be of good to excellent quality and a NIIRS mean of 1.6 indicates that the images are interpretable. Our interpretation of the imagery shows that the data has considerable potential for use in geo-visualization and cartographic land use and land cover mapping applications. The image analysis also showed the capability of the nSight-1 sensor to capture features related to structural geology, geomorphology and topography quite prominently.

Machine learning for quality assessment of ground-based optical images of satellites

Astronomical images collected by ground-based telescopes suffer from degradation and perturbations attributed to atmospheric turbulence. We investigate the application of convolutional neural networks (CNNs) to ground-based satellite imaging to address two existing problems. First, the multiframe blind deconvolution (MFBD) algorithms that can extract well-resolved images from these degraded data frames are computationally expensive, requiring supercomputing infrastructure for relatively fast performance and currently cannot be done in real time. Because of this, it is difficult to optimize collection parameters to maximize the likelihood of producing a resolved image with MFBD. Second, the space-object National Imagery Interpretability Rating Scale (SNIIRS) allows human analysts to provide a quantitative score of image quality based on identification of target features. It is naturally difficult to automate this scoring process, not only because the scale is based on identifiable features but also because the images may be in an almost-resolved image quality regime that is difficult to handle for traditional computer vision techniques. For both applications, we present our results using CNNs on data collected at the Maui Space Surveillance Site as well as a new synthetic dataset we introduce containing over a million SNIIRS rated pairings of perturbed and pristine ground-based satellite images.

EO Sensor Planning for UAV Engineering Reconnaissance Based on NIIRS and GIQE

When unmanned aerial vehicles (UAVs) support the Corps of Engineers in reconnaissance operations, in order to gather visible image information that should meet the mission’s need, we grouped the engineering reconnaissance information interpretation tasks into 10 levels by using the National Imagery Interpretability Rating Scale (NIIRS). The quantitative relationship between the engineering targets, sensor performance, and flight altitude was established through the general image quality equation (GIQE) and the geometrical property of the ground sampled distance (GSD). Through some simulations, the influence of variable factors of the EO sensor imaging quality was analyzed, and the imaging height of the sensor for an engineering reconnaissance scenario was calculated. The results showed that this study could solve the problem of poor image quality caused by the flight altitude not meeting the mission requirements.

Prediction of compression-induced image interpretability degradation

Image compression is an important component in modern imaging systems as the volume of the raw data collected is increasing. To reduce the volume of data while collecting imagery useful for analysis, choosing the appropriate image compression method is desired. Lossless compression is able to preserve all the information, but it has limited reduction power. On the other hand, lossy compression, which may result in very high compression ratios, suffers from information loss. We model the compression-induced information loss in terms of the National Imagery Interpretability Rating Scale or NIIRS. NIIRS is a user-based quantification of image interpretability widely adopted by the Geographic Information System community. Specifically, we present the Compression Degradation Image Function Index (CoDIFI) framework that predicts the NIIRS degradation (i.e., a decrease of NIIRS level) for a given compression setting. The CoDIFI-NIIRS framework enables a user to broker the maximum compression setting while maintaining a specified NIIRS rating.

User-Oriented Image Quality Assessment of ZY-3 Satellite Imagery

For the purpose of evaluating the image quality of ZY-3 (Ziyuan-3) satellite imagery and from the perspective of data users in various professions, this paper proposed a user-oriented quality assessment to assess the panchromatic and multispectral images of ZY-3, which included the assessment of its image statistical characteristics, image visual effect, and interpretability. In order to better understand the quality level of the ZY-3 image product and the scope of its applications, a comparison of image quality factors was conducted between ZY-3 image and SPOT-5 image due to the close similarity between their functionalities. As far as the ZY-3 image was concerned, features of its gray-scale statistics showed the advantage in its wider data range, higher standard deviation, and a better histogram presenting in image. The analysis of texture from the two images also revealed better performance of the ZY-3 image than that of SPOT-5 with respect to homogeneity (HOM), contrast (CON), entropy (ENT), and angular second moment (ASM). Moreover, the result of visual effect assessment also confirmed the good performance of ZY-3 data. An evaluation of the interpretability was conducted by using the National Imagery Interpretability Rating Scale (NIIRS). According to the relationship between NIIRS and mapping scale, the suitable mapping scales of the images from both ZY-3 and SPOT-5 were estimated from the viewpoint of image quality. As our survey shows, ZY-3 image products are marked by good gray scale characteristics and texture effect. According to its moderate NIIRS rating and result of mapping scale estimation, its maximum mapping scale potential is about 1:16 000.

ANALYSIS OF THE EFFECTS OF IMAGE QUALITY ON DIGITAL MAP GENERATION FROM SATELLITE IMAGES

Abstract. High resolution satellite images are widely used to produce and update a digital map since they became widely available. It is well known that the accuracy of digital map produced from satellite images is decided largely by the accuracy of geometric modelling. However digital maps are made by a series of photogrammetric workflow. Therefore the accuracy of digital maps are also affected by the quality of satellite images, such as image interpretability. For satellite images, parameters such as Modulation Transfer Function(MTF), Signal to Noise Ratio(SNR) and Ground Sampling Distance(GSD) are used to present images quality. Our previous research stressed that such quality parameters may not represent the quality of image products such as digital maps and that parameters for image interpretability such as Ground Resolved Distance(GRD) and National Imagery Interpretability Rating Scale(NIIRS) need to be considered. In this study, we analyzed the effects of the image quality on accuracy of digital maps produced by satellite images. QuickBird, IKONOS and KOMPSAT-2 imagery were used to analyze as they have similar GSDs. We measured various image quality parameters mentioned above from these images. Then we produced digital maps from the images using a digital photogrammetric workstation. We analyzed the accuracy of the digital maps in terms of their location accuracy and their level of details. Then we compared the correlation between various image quality parameters and the accuracy of digital maps. The results of this study showed that GRD and NIIRS were more critical for map production then GSD, MTF or SNR.

NIIRS 추정을 위한 자연표적 기반의 에지분석기법 개발

NIIRS(National Imagery Interpretability Rating Scale)는 영상의 판독력을 설명하는 품질 지표로 고해상도 위성영상의 품질을 나타내는데 널리 사용되어 왔다. 이는 MTF(Modulation Transfer Function), SNR(Signal to Noise Ratio), 또는 GSD(Ground Sampling Distance)와는 달리 객관적이고, 직관적으로 영상의 전반적인 품질을 설명할 수 있다는 점에서, 그 활용도가 매우 크다고 할 수 있다. NIIRS는 전문 판독가에 의해 육안으로 직접 측정되거나, 영상의 에지 분석(Edge analysis)을 통해 정확하게 추정할 수 있다. 일반적으로 에지 분석에는 품질 측정을 위해 특별히 제작된 인공의 표적이 사용된다. 이 인공표적의 일례로 특정 크기의 반사율 차이를 가지는 흑백 패턴의 천막 형태가 있으며, 이러한 인공표적은 인공위성의 촬영경로 상에 설치되어 진다. 이 때문에 인공표적을 이용하는 방식은 표적의 설치와 관련하여 많은 비용이 지출될 뿐 아니라, 수시로 수행될 수 없다는 문제점을 가지게 된다. 이에 본 논문에서는 영상에서 쉽게 관측될 수 있는 자연표적들로부터 정확한 NIIRS 추정이 가능한 새로운 방식의 에지 분석법을 제안하였다. 이 방법은 임의의 성질을 가지는 자연표적의 특성을 반영하기 위해 알고리즘의 강인성이 강조되었으며, 다양한 실험들을 통해 성능이 평가되었다. 실험 결과는 제안 알고리즘이 기존 방식의 대안으로서 충분히 활용 가능함을 보여 주었다. As one measure of image interpretability, NIIRS(National Imagery Interpretability Rating Scale) has been used. Unlike MTF(Modulation Transfer Function), SNR(Signal to Noise Ratio), and GSD(Ground Sampling Distance), NIIRS can describe the quality of overall image at user's perspective. NIIRS is observed with human observation directly or estimated by edge analysis. For edge analysis specially manufactured artificial target is used commonly. This target, formed with a tarp of black and white patterns, is deployed on the ground and imaged by the satellite. Due to this, the artificial target-based method needs a big expense and can not be performed often. In this paper, we propose a new edge analysis method that enables to estimate NIIRS accurately. In this method, natural targets available in the image are used and characteristics of the target are considered. For assessment of the algorithm, various experiments were carried out. The results showed that our algorithm can be used as an alternative to the artificial target-based method.

Advanced image processing and wavefront sensing with real-time phase diversity

This paper will describe a state-of-the-art approach to real-time wavefront sensing and image enhancement. It will explore Boeing's existing technology to realize a 50 Hz frame rate (with a path to 1 KHz and higher). At this higher rate, phase diversity will be readily applicable to compensate for distortions of large dynamic bandwidth such as those of the atmosphere. We will describe various challenges in aligning a two-camera phase diversity system. Such configurations make it almost impossible to process the captured images without additional upgrade in the algorithm to account for alignment errors. An example of an error is the relative misalignment of the two images, the "best-focus" and the diversity image, where it is extremely hard to maintain alignment to less than a fraction of 1 pixel. We will show that the algorithm performance increases dramatically when we account for these errors in the estimation process. Preliminary evaluation has assessed a National Imagery Interpretability Rating Scale increase of approximately 3 from the best-focus to the enhanced image. Such a performance improvement would greatly increase the operating range (or, equivalently, decrease the weight) of many optical systems.

Developing an interpretability scale for motion imagery

The motion imagery community would benefit from standard measures for assessing image interpretability. The National Imagery Interpretability Rating Scale (NIIRS) has served as a community standard for still imagery, but no comparable scale exists for motion imagery. Several considerations unique to motion imagery indicate that the standard methodology employed in the past for NIIRS development may not be applicable or, at a minimum, requires modifications. The dynamic nature of motion imagery introduces a number of factors that do not affect the perceived interpretability of still imagery—namely target motion and camera motion. We conducted a series of evaluations to understand and quantify the effects of critical factors. This paper presents key findings about the relationship of perceived interpretability to ground sample distance, target motion, camera motion, and frame rate. Based on these findings, we modified the scale development methodology and validated the approach. The methodology adapts the standard NIIRS development procedures to the softcopy exploitation environment and focuses on image interpretation tasks that target the dynamic nature of motion imagery. This paper describes the proposed methodology, presents the findings from a methodology assessment evaluation, and offers recommendations for the full development of a scale for motion imagery.

IKONOS spatial resolution and image interpretability characterization

Five individual projects characterized the spatial performance of the IKONOS commercial imaging sensor. The result was determination of the spatial image quality of IKONOS data products in terms of the National Imagery Interpretability Rating Scale (NIIRS), the system Modulation Transfer Function (MTF), the system stability over its first year of operation, the characteristics of the Space Imaging MTF Compensation (MTFC) procedure, and the application-specific capabilities of IKONOS imagery. Both panchromatic and multispectral imagery were evaluated. Major conclusions of this work are that the system was stable in imaging performance during the first year of operation, that its MTF meets the specification for the NASA Scientific Data Purchase program, that the initial MTFC processing appears to be transposed in the in-track and the cross-track directions, that the MTFC results in a noise amplification of 2× to 4× in addition to sharpening the imagery, and that IKONOS panchromatic imagery achieves an average NIIRS rating of 4.5.

Interpretability performance assessment of JPEG2000 and part 1 compliant region of interest coding

JPEG2000 is a new international standard for still image coding and provides an extensive set of features vital to many high-end and emerging applications. The default performance metric for JPEG2000 is an optimisation of objective quality versus bit-rate. However, for high compression applications, it would be more appropriate that regions of interest are prioritised for interpretability. JPEG2000 allows for this in its region of interest (ROI) feature. Part 1 of JPEG2000 provides two different ROI coding mechanisms, namely the max-shift and implicit method. With these coders, the objective quality across the image may be selectively sacrificed to achieve higher interpretability in regions of interest. This principle is quantitatively tested using interpretability ratings, assigned using a subjective National Imagery Interpretability Rating Scale (NIIRS), and ROI coding methods are compared in this capacity. Results show that both the max-shift and the implicit method have advantages and disadvantages relative to one another and the choice of methods for ROI coding is very much dependant on the requirements of the application at hand.

Synthetic aperture radar target acquisition model based on a National Imagery Interpretability Rating Scale to probability of discrimination conversion

Previous efforts have established models that relate IR and visible imaging sensor design and operating parameter values to measures of information extraction performance. The surveillance and reconnaissance community predicts National Imagery Interpretability Rating Scale (NIIRS) values using the general image quality equation (GIQE). The target acquisition community predicts probabilities of discrimination (detection, recognition, identification) using various minimum resolvable contrast/minimum resolvable temperature (MRC/MRT) models, and methods have been developed to link the two sets of predictions. No such models currently exist for the radar domain, although a radar NIIRS has been developed. We provide a basis for initial estimates of synthetic aperture radar (SAR) target acquisition performance. First, a conversion from radar to IR (and visible) NIIRS is provided. The IR NIIRS is then converted to probabilities of discrimination values. The two conversions together provide rough estimates of SAR target acquisition performance as a function of radar NIIRS values.

Evaluation of the tactical utility of compressed imagery

The effects of compression on image utility are assessed based on manual exploitation performed by military imagery analysts (IAs). The original, uncompressed synthetic aperture radar imagery and compressed products are rated for the Radar National Imagery Interpretability Rating Scale (NIIRS), image features and sensor artifacts, and target detection and recognition. Images were compressed via standard JPEG compression, single-scale intelligent bandwidth compression (IBC), and wavelet/trellis-coded quantization (W/TCQ) at 50-to-1 and 100-to-1 ratios. We find that the utility of the compressed imagery differs only slightly from the uncompressed imagery, with the exception of the JPEG products. Otherwise, both the 50-to-1 and 100-to-1 compressed imagery appear similar in terms of image quality. Radar NIIRS indicates that even 100-to-1 compression using IBC or W/TCQ has minimal impact on imagery intelligence value. A slight loss in performance occurs for vehicle counting and identification tasks. These findings suggest that both single-scale IBC and W/TCQ compression techniques have matured to a point that they could provide value to the tactical user. Additional assessments may verify the practical limits of compression for synthetic aperture radar (SAR) data and address the transition to a field environment.

Comparison of SNR image quality metrics for remote sensing systems

Different definitions of the signal-to-noise ratio (SNR) are be- ing used as metrics to describe the image quality of remote sensing systems. It is usually not clear which SNR definition is being used and what the image quality of the system is when an SNR value is quoted. This paper looks at several SNR metrics used in the remote sensing community. Image simulations of the Kodak Space Remote Sensing Camera, Model 1000, were produced at different signal levels to give insight into the image quality that corresponds with the different SNR metric values. The change in image quality of each simulation at different signal levels is also quantified using the National Imagery Interpretability Rating Scale (NIIRS) and related to the SNR metrics to better under- stand the relationship between the metric and image interpretability. An analysis shows that the loss in image interpretability, measured as DNI- IRS, can be modeled as a linear relationship with the noise-equivalent change in reflection (NE Dr). This relationship is used to predict the val- ues that the various SNR metrics must exceed to prevent a loss in the interpretability of the image from the noise. © 2001 Society of Photo-Optical

General image-quality equation for infrared imagery

The regression-based general image-quality equation (GIQE) previously developed for visible imagery was extended to IR imagery. The equation predicts IR National Imagery Interpretability Rating Scale values as a function of scale, sharpness, and signal-to-noise ratio with the same form as the visible GIQE.

Sensor performance conversions for infrared target acquisition and intelligence–surveillance–reconnaissance imaging sensors

Target acquisition infrared imaging sensors are characterized by their minimum resolvable temperature parameter that is translated to the probability of identification (Pid) performance estimate for a given target. Intelligence-surveillance-reconnaissance (ISR) sensors are characterized by the general image quality equation to give a national imagery interpretability rating scale (NIIRS) performance estimate. Sensors, such as those on Predator and Global Hawk, will soon be used for both ISR and target acquisition purposes. We present a performance conversion that includes both sensor resolution and sensitivity. We also provide the first empirical results to our knowledge ever to be presented that relate NIIRS and Pid for a given set of targets.

Understanding image quality losses due to smear in high-resolution remote sensing imaging systems

Space-based high-resolution scanning array imaging sys- tems have the potential to introduce large amounts of image smear. When designing these systems, it is useful to understand how smear will degrade image quality. A brief description of the causes of smear and a simple mathematical model are presented. A series of image simulations (for a system in which lFN/p51.0, where l is the mean wavelength for a panchromatic system, FN is the system f number, and p is the pixel pitch of the detectors) are performed in which along scan smear (ranging from 1.0 to 8.0 pixels) is introduced. Using the National Imagery Inter- pretability Rating Scale (NIIRS), expert observers rated DNIIRS differ- ence in image quality between the images with simulated smear and the original ''unsmeared'' image. The functional relationship between smear error and image quality (in units of DNIIRS) is determined. © 1999 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(99)01505-6)