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Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

As an emerging cash crop, industrial hemp (Cannabis sativa L.) grown for cannabidiol (CBD) has spurred a surge of interest in the United States. Cultivar selection and harvest timing are important to produce CBD hemp profitably and avoid economic loss resulting from the tetrahydrocannabinol (THC) concentration in the crop exceeding regulatory limits. Hence there is a need for differentiating CBD hemp cultivars and growth stages to aid in cultivar and genotype selection and optimization of harvest timing. Current methods that rely on visual assessment of plant phenotypes and chemical procedures are limited because of its subjective and destructive nature. In this study, hyperspectral imaging was proposed as a novel, objective, and non-destructive method for differentiating hemp cultivars, growth stages as well as plant organs (leaves and flowers). Five cultivars of CBD hemp were grown greenhouse conditions and leaves and flowers were sampled at five growth stages 2–10 weeks in 2-week intervals after flower initiation and scanned by a benchtop hyperspectral imaging system in the spectral range of 400–1000 nm. The acquired images were subjected to image processing procedures to extract the spectra of hemp samples. The spectral profiles and scatter plots of principal component analysis of the spectral data revealed a certain degree of separation between hemp cultivars, growth stages, and plant organs. Machine learning based on regularized linear discriminant analysis achieved the accuracy of up to 99.6% in differentiating the five hemp cultivars. Plant organ and growth stage need to be factored into model development for hemp cultivar classification. The classification models achieved 100% accuracy in differentiating the five growth stages and two plant organs. This study demonstrates the effectiveness of hyperspectral imaging for differentiating cultivars, growth stages and plant organs of CBD hemp, which is a potentially useful tool for growers and breeders of CBD hemp. [ABSTRACT FROM AUTHOR]

The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.g., number of locations and frequency of data acquisition) and throughput. In this study, phenomics technologies were applied to evaluate the performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017–2019. The images were analyzed semi-automatically with custom image processing algorithm and features were extracted, such as canopy area and summary statistics associated with vegetation indices. The study demonstrated significant correlations (P < 0.05) between image-based features (e.g., canopy area and sum normalized difference vegetation index) with yield (r up to 0.93 and 0.85 for chickpea and dry pea, respectively), days to 50% flowering (r up to 0.76 and 0.85, respectively), and days to physiological maturity (r up to 0.58 and 0.84, respectively). Using image-based features as predictors, seed yield was estimated using least absolute shrinkage and selection operator regression models, during which, coefficients of determination as high as 0.91 and 0.80 during model testing for chickpea and dry pea, respectively, were achieved. The study demonstrated the feasibility to monitor agronomic traits and predict seed yield in chickpea and dry pea breeding trials across multiple locations and seasons using phenomics tools. Phenomics technologies can assist plant breeders to evaluate the performance of breeding materials more efficiently and accelerate breeding programs. [ABSTRACT FROM AUTHOR]

The magnetic domain structure is an important factor in discussing the coercivity development mechanism. We have researched on the new observation method of magnetic domain structures using multiple distances observation of magnetic force microscope (MFM) and image processing. We observed magnetic domain structures in thermally demagnetized state and alternating current (ac) demagnetized state of ferrite-sintered magnets using this method. In addition, the initial magnetization curve was measured in each demagnetized state. There were differences in the domain structures and the initial magnetization curves in each demagnetized state. These differences suggest that irreversible multi-domain gains (IRMDGs) confirmed in rare-earth magnets are present even in sintered ferrite magnets. [ABSTRACT FROM AUTHOR]

Teleseismic receiver functions (RFs) are frequently used to determine depths of seismic discontinuities in the crust and upper mantle. We developed an efficient reverse-time migration (RTM) method that is applied to teleseismic receiver functions directly. Both the primary P -to- S converted phases and their crustal multiples in RFs can be used for imaging seismic discontinuities. The method uses the phase-shift-plus-interpolation algorithm to extrapolate both the source and receiver wavefields in a 3-D velocity model, which greatly reduces the computation costs compared with those using a full wave-equation numerical solver. Tests using synthetic data in various crustal models demonstrate the effectiveness of the method and its superiority over the common-conversion-point stacking method. In particular, the method handles diffraction caused by strong lateral structural variations correctly and there is no limitation on the maximum dip of the interface. We applied the method to real data of a linear array in the Wabash Valley Seismic Zone in the central USA and obtained a crustal structural image across a failed continental rift. We suggest that future passive-source seismic recording experiments for crustal scale imaging use station spacing less than 5 km, and a 2-D array with even smaller station spacing is desired for regions with strong lateral structural variations. With increasing numbers of sensors used in passive-source recording experiments nowadays, our RF-RTM method can be a useful tool for structural imaging on scales ranging from sedimentary basins, crust to lithosphere. [ABSTRACT FROM AUTHOR]

The widespread use of social media has created a valuable but underused source of data for the environmental sciences. We demonstrate the potential for images posted to the website Twitter to capture variability in vegetation phenology across United States National Parks. We process a subset of images posted to Twitter within eight U.S. National Parks, with the aim of understanding the amount of green vegetation in each image. Analysis of the relative greenness of the images show statistically significant seasonal cycles across most National Parks at the 95% confidence level, consistent with springtime green-up and fall senescence. Additionally, these social media-derived greenness indices correlate with monthly mean satellite NDVI (r = 0.62), reinforcing the potential value these data could provide in constraining models and observing regions with limited high quality scientific monitoring. [ABSTRACT FROM AUTHOR]

Pavement markings provide guidance to road travelers and, when not properly removed during construction projects, can lead to fatal accidents. Current state guidelines, however, do not specify a level of acceptable damage and there is no reliable inspection tool available other than on-site individual visual inspections. To address these shortcomings, the main objective of this study was to identify effective temporary marking removal methods on pavements in roadway construction zones. Unlike previous studies, this research tested an extensive number of removal methods on both concrete and asphalt pavements, especially including dry-ice blasting and environmentally friendly non-MeCl chemical stripping. The methods were selected based on a nationwide survey and literature review. In addition, an innovative removal inspection method was developed using image-processing technology to accurately measure the visual damage to the pavement. Among the tested removal methods, the new non-MeCl chemical stripping method was most satisfactory in terms of performance quality. This study of pavement marking removal and inspection can help state transportation agencies significantly improve the current temporary marking removal practices and inspection protocols. [ABSTRACT FROM AUTHOR]

Abstract: Automated alignment for the National Ignition Facility (NIF) is accomplished using a large-scale parallel control system that directs 192 laser beams along the 300m optical path. The beams are then focused down to a 50μ spot in the middle of the target chamber. The entire process is completed in less than 50min. The alignment system commands 9000 stepping motors for extremely precise adjustment of mirrors and other optics. 41 control loops per beamline perform parallel processing services running on a LINUX cluster to analyze high-resolution images of the beams and their references. This paper describes the status the NIF automatic alignment system and the challenges encountered as NIF development has transitioned from building the laser, to becoming a research project supporting a 24h, 7 days/week laser facility. NIF is now a continuously operated system where performance monitoring is increasingly more critical for operation, maintenance, and commissioning tasks. Equipment wear and the effects of high energy neutrons from fusion experiments are issues which affect alignment efficiency and accuracy. New sensors needing automatic alignment assistance are common. System modifications to improve efficiency and accuracy are prevalent. Handling these evolving alignment and maintenance needs while minimizing the impact on NIF experiment schedule is expected to be an on-going challenge for the planned 30 year operational life of NIF. [Copyright &y& Elsevier]

We clarify that the derivative-free fan-beam reconstruction formula [IEEE Trans. Image Process. 2, 543–547, 1993] only allows exact reconstruction of an object for a circular trajectory or at the origin of the coordinate system for a radially symmetric noncircular trajectory. [ABSTRACT FROM PUBLISHER]

In the late 1990s, image processing was first applied to steel bridge coating assessment in the United States. Yet, there has not been any robust method that could solve nonuniform illumination problems to date. In this regard, this paper aims to develop an approach that handles nonuniform illumination in rust image recognition. The proposed approach also contributes to rust intensity recognition and process automation. This paper adopts the a*b* color configuration (of the L*a*b* color space) which is shown to be the best coordinate system for rust recognition by the proposed method. Different rust colors are regarded as relating to different degrees of rust intensity. Due to the difficulty of defining light rust colors, the adaptive-network-based fuzzy inference system (ANFIS) is used as the framework for the box-and-ellipse-based ANFIS (BE-ANFIS). Illumination adjustment is used to overcome the nonuniform illumination problem. The proposed BE-ANFIS is trained using 120 rust images of the size of 256×256 pixels. The performance of BE-ANFIS is compared to the fuzzy C-means (FCM) method, one of the most popular image recognition methods, and the adaptive ellipse approach (AEA) which is the writers’ previous approach. Based on testing 35 rust images, the results show that BE-ANFIS can better recognize rust intensity (i.e., distinguishing light and dark rusts) than FCM and AEA. [ABSTRACT FROM AUTHOR]

In this paper we present a method for the recognition of hand- written digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented. The classification task is performed using a Convolutional Neural Net- work (CNN). CNN is a special type of multi-layer neural network, being trained with an optimized version of the back-propagation learning algorithm. CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters), and with robustness to distortions and simple geometric transformations. The main contributions of this paper are related to the original methods for increasing the efficiency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background. By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology) database. [ABSTRACT FROM AUTHOR]

Abstract: NASA''s Cassini project has conducted a series of innovative “Scientist for a Day” outreach sessions with great success. Individual students and classrooms are presented, via the Cassini web site, with a selection of images that could be taken by the Cassini spacecraft in orbit around Saturn. Student essays are submitted indicating their preferred image, along with a detailed description of why that image was selected, often drawing heavily from material found elsewhere in the Cassini web resources. Winners from different age categories are chosen, and the selected images are sequenced. Very soon after the images reach the ground, winning classrooms are invited to participate via tele- and video-con with key Cassini scientists and engineers, where interactive discussions take place. This program has been very successful and is considered a key outreach highlight of Cassini and JPL in general. It reaches a significant number of students, involves them directly in image planning, and engages them in a highly interactive and inspirational environment requiring detailed study and creative thought. [Copyright &y& Elsevier]

The article presents an overview of the work and research being done by the organizations in the area of metadata creation related to cataloging and preservation of moving image content in the U.S. It highlights several organizations involved in creating metadata for moving images including the Motion Picture Experts Group (MPEG) and the Corporation for Public Broadcasting (CPB). Problems and suggestions encountered in the field of cataloging and preservation of moving images is also outlined.

The article reports on the Stennis Space Center's (SSC) Applied Science and Technology Project Office (ASTPO) and its use of the NASA's remote sensing, image processing, and oceanography to address issues related to Coastal Gulf of Mexico. ASTPO is a part of the NASA Applied Sciences Program which aims to demonstrate the utility of NASA Earth Science data, models, tools and techniques. It focuses on remote sensing and other tools of NASA for Gulf of Mexico applications. It also cooperates with the Gulf of Mexico Alliance (GOMA) and on the National Oceanic and Atmospheric Administration (NOAA) to ensure that the issues of the Gulf of Mexico community are accurately and appropriately identified.

This research presents a segmentation-based, image processing method to automate the extraction of tidal datum referenced shorelines from airborne light detection and ranging (LIDAR) data. The method first segments a LIDAR digital elevation model (DEM) into a binary image, consisting of land and water pixels, by intersecting the LIDAR DEM and the tidal datum surface. A chain of image processing algorithms, including region grouping and labeling, two passes of image region scanning, a mathematical morphology operation, line tracing, and vectorization, is sequentially applied to the segmented binary image. Our applications to the upper Texas gulf coast show that the method is efficient, accurate, objective, and replicable. Spatially detailed shorelines can be derived from the LIDAR data with minimal human intervention. The method is a substantial technical improvement over standard cross-shore profile and contouring methods. We also examine and quantify the effects of vertical measurement error of the LIDAR system and the uncertainty in tidal datum determination on the shoreline extraction process using the Monte Carlo simulation technique. The Monte Carlo technique allows confidence intervals and summary error statistics to be calculated for each section of the extracted shoreline. Our analysis suggests that the horizontal position of the MHW shoreline derived from the LIDAR data is accurate within 4.5 m at the 95% confidence level. [ABSTRACT FROM AUTHOR]

The article discusses the techniques used in locating a specific type of image or file in the U.S. Because of the existence of different types of images which varies in color, type and description, it is difficult for user to locate a specific image that he wants. Previously, the Library of Congress uses Thesaurus for Graphic Materials I and Thesaurus for Graphic Materials II in cataloguing and retrieving images. With the onset of new technology, collaborative tagging is used in assigning keywords to textual documents and image resources. Such a tool is used by sharing sites such as Flick because it allows massive image storage and web diffusion wherein users upload their own images and index them using their own tags.

This article is my personal account on the work at Stanford on Stanley, the winning robot in the DARPA Grand Challenge. Between July 2004 and October 2005, my then-postdoc Michael Montemerlo and I led a team of students, engineers, and professionals with the single vision of claiming one of the most prestigious trophies in the field of robotics: the DARPA Grand Challenge (DARPA 2004). The Grand Challenge, organized by the U.S. government, was unprecedented in the nation's history. It was the first time that the U.S. Congress had appropriated a cash price for advancing technological innovation. My team won this prize, competing with some 194 other teams. Stanley was the fastest of five robotic vehicles that, on October 8, 2005, successfully navigated a 131.6-mile-long course through California's Mojave Desert. This essay is not about the technology behind our success; for that I refer the interested reader to recent articles on the technical aspects of Stanley (Dahlkamp et al. 2006; Montemerlo et al. 2006; Stavens and Thrun 2006; Thrun, Montemerlo, and Aron 2006; Thrun et al. 2006). Instead, this is my personal story of leading the Stanford Racing Team. It is the story of a team of people who built an autonomous robot in record time. It is also a success story for the field of artificial intelligence, as Stanley used some state of the art AI methods in areas such as probabilistic inference, machine learning, and computer vision. Of course, it is also the story of a step towards a technology that, one day, might fundamentally change our lives. [ABSTRACT FROM AUTHOR]

Prostatic adenocarcinoma is the most commonly occurring cancer among men in the United States, second only to skin cancer. Currently, the only definitive method to ascertain the presence of prostatic cancer is by trans-rectal ultrasound (TRUS) directed biopsy. Owing to the poor image quality of ultrasound, the accuracy of TRUS is only 20%-25%. High-resolution magnetic resonance imaging (MRI) has been shown to have a higher accuracy of prostate cancer detection compared to ultrasound. Consequently, several researchers have been exploring the use of high resolution MRI in performing prostate biopsies. Visual detection of prostate cancer, however, continues to be difficult owing to its apparent lack of shape, and the fact that several malignant and benign structures have overlapping intensity and texture characteristics. In this paper, we present a fully automated computer-aided detection (CAD) system for detecting prostatic adenocarcinoma from 4 Tesla ex vivo magnetic resonance (MR) imagery of the prostate. After the acquired MR images have been corrected for background inhomogeneity and nonstandardness, novel three-dimensional (3-D) texture features are extracted from the 3-D MRI scene. A Bayesian classifier then assigns each image voxel a "likelihood" of malignancy for each feature independently. The "likelihood" images generated in this fashion are then combined using an optimally weighted feature combination scheme. Quantitative evaluation was performed by comparing the CAD results with the manually ascertained ground truth for the tumor on the MRI. The tumor labels on the MR slices were determined manually by an expert by visually registering the MR slices with the corresponding regions on the histology slices. We evaluated our CAD system on a total of 33 two-dimensional (2-D) MR slices from five different 3-D MR prostate studies. Five slices from two different glands were used for training. Our feature combination scheme was found to outperform the individual texture features, and also other popularly used feature combination methods, including AdaBoost, ensemble averaging, and majority voting. Further, in several instances our CAD system performed better than the experts in terms of accuracy, the expert segmentations being determined solely from visual inspection of the MRI data. In addition, the intrasystem variability (changes in CAD accuracy with changes in values of system parameters) was significantly lower than the corresponding intraobserver and interobserver variability. CAD performance was found to be very similar for different training sets. Future work will focus on extending the methodology to guide high-resolution MRI-assisted in vivo prostate biopsies. [ABSTRACT FROM AUTHOR]

Most RIS and PACS systems include extensive auditing capabilities as part of their security model, but inspecting those adult logs to obtain useful information can be a daunting task. Manual analysis of audit trails, though cumbersome, is often resorted to because of the difficulty to construct queries to extract complex information from the adult logs. The approach proposed by the authors uses standard off-the shelf multidimensional analysis software tools to assist the PACS/RIS administrator and/or security officer in analyzing those audit logs to identify and scrutinize suspicious events. Large amounts of data can be quickly reviewed and graphical analysis tools help explore system utilization. While additional efforts are required to fully satisfy the demands of the ever-increasing security and confidentiality pressures, multidimensional analysis tools are a practical step toward actually using the information that is already being captured in the system's audit logs. In addition, once the work is performed to capture and manipulate the audit logs into a viable format for the multidimensional analysis tool, it is relatively easy to extend the system to incorporate other pertinent data, thereby enabling the ongoing analysis of other aspects of the department's workflow. [ABSTRACT FROM AUTHOR]

This article describes the development of Image Processing and Analysis Group at Yale University in New Haven, Connecticut and the broad range of work in the area of medical image analysis that has been pursued. During the 1970s, several doctors of philosophy formed a group, with an aim to provide physics support for the clinical operations in the burgeoning fields of nuclear medicine, ultrasound and computed tomography. As 1990 approached, the group began to explore more deeply some of the key areas in medical image analysis. The group also works on using organ and tissue-level diagnostic function and imaging registration.

Presents the details of the panel discussions at the PACS 1982 Conference in the U.S. Panel members; List of participants; Topics discussed.

Focuses on detecting vegetation responses to interannual climate using the airborne visible-infrared imaging spectrometer (AVIRIS) data over a natural savanna in the Central Coast Range in California. Information on the two AVIRIS images chosen for the study; Method used to detect and quantify changes between years; Unmixing results using three endmembers.

Previews the conference called 'Image and Meaning: Envisioning and Communicating Science and Technology,' which will be held June 13-16, 2001, in Cambridge, Massachusetts. Emphasis on the promises and pitfalls of imaging in science; Role of computer-aided tools and technologies in giving images a higher profile; Complementary ways of viewing reality.

This article evaluates the state-of-the-art technology of the Video Image Processing (VIP) systems. The study investigates the capabilities, accuracy, costs, and limitations of VIP. Comparison is made for competing traffic detector technologies. The potential uses of VIP for Advance Traffic Management Systems (ATMS) are analyzed. This is a fast-developing technology and its continuous evaluation and review are essential for the Intelligent Transportation Systems (ITS) programs and research. [ABSTRACT FROM AUTHOR]

The author discusses the Q methodology associated in advertising in the U.S. He reflects with the use of Q methodology in image making for political purposes. He discusses the relevance of several books such as "The Play Theory of Mass Communication" in 1967 and "Communication Yearbook 4" and the television program "The Day After."

The article focuses on the life and career of Augustin Le Prince, an inventor who is generally recognized as the first person to record motion images on film. In October 1888, Le Prince filmed roundhay garden scene, which is thought to be the world's first successful attempt to record moving images, and hence the very first motion picture film. He followed this with a film of a Leeds bridge street scene. These were several years before the work of competing inventors such as Thomas Edison. He was never able to perform a planned public demonstration in the U.S. because he mysteriously vanished from a train in 1890.

Purpose: At present, Argus II is the only retinal prosthesis approved by the US Food and Drug Administration that induces visual percepts in people who are blind from end-stage outer retinal degenerations such as retinitis pigmentosa. It has been shown to work well in sparse, high-contrast settings, but in daily practice visual performance with the device is likely to be hampered by the cognitive load presented by a cluttered real-world environment. In this study, we investigated the effect of a stereo-disparity-based distance-filtering system on four experienced Argus II users for a range of tasks: object localization, depth discrimination, orientation and size discrimination, and people detection and direction of motion., Methods: Functional vision was assessed in a semicontrolled setup using unfiltered (normal camera) and distance-filtered (stereo camera) imagery. All tasks were forced choice designs and an extension of signal detection theory to latent (unobservable) variables was used to analyze the data, allowing estimation of person ability (person measures) and task difficulty (item measures) on the same axis., Results: All subjects performed better with the distance filter compared with the unfiltered image ( P   < 0.001 on all tasks except localization)., Conclusions: Our results show that depth filtering using a disparity-based algorithm has significant benefits for people with Argus II implants., Translational Relevance: The improvement in functional vision with the distance filter found in this study may have an important impact on vision rehabilitation and quality of life for people with visual prostheses and ultra low vision., Competing Interests: Disclosure: A. Kartha, None; R. Sadeghi, None; M.P. Barry (E); C. Bradley, None; P. Gibson (E); A. Caspi (C, P); A. Roy (E); G. Dagnelie (C, P), (Copyright 2020 The Authors.)

In this study an initial validation of the Landsat 8 (L8) Operational Land Imager (OLI) Surface Reflectance (SR) product was performed. The OLI SR product is derived from the L8 Top-of-Atmosphere product via the Landsat Surface Reflectance Code (LaSRC) software and generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of this study is to develop and evaluate proper validation methodology for the OLI L2 SR product. Validation was performed using near-simultaneous ground truth SR measurements during Landsat 8 overpasses at 13 sites located in the U.S., Brazil, Chile and France. The ground truth measurements consisted of field spectrometer measurements, automated hyperspectral ground measurements operated by the Radiometric Calibration Network (RadCalNet) and derived SR measurements from Airborne Observation Platforms (AOP) operated by the National Ecological Observatory Network (NEON). The 13 sites cover a broad range of 0–0.5 surface reflectance units across the reflective solar spectrum. Results show that the mean reflectance difference between OLI L2 SR products and ground truth measurements for the 13 validation sites and all bands was under 2.5%. The largest uncertainties of 11% and 8% were found in the CA and Blue bands, respectively; whereas, the longer wavelength bands were within 4% or less. Results consistently indicated similarity between the OLI L2 SR product and ground truth data, especially in longer wavelengths over dark and bright targets, while less reliable performance was observed in shorter wavelengths and sparsely vegetated targets. [ABSTRACT FROM AUTHOR]