Digital Imaging And Remote Sensing Image Generation Research Articles

Image Collection Simulation Using High-Resolution Atmospheric Modeling

A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

Simulations of Leaf BSDF Effects on Lidar Waveforms

Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45° interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data.

Tracking in Aerial Hyperspectral Videos Using Deep Kernelized Correlation Filters

Hyperspectral imaging holds enormous potential to improve the state of the art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multimodal hyperspectral sensors have attracted growing interest due to their ability to record extended data quickly from aerial platforms. In this paper, we apply popular concepts from traditional object tracking, namely, kernelized correlation filters (KCFs) and deep convolutional neural network features to aerial tracking in the hyperspectral domain. We propose the deep hyperspectral KCF-based tracker (DeepHKCF) to efficiently track aerial vehicles using an adaptive multimodal hyperspectral sensor. We address low temporal resolution by designing a single KCF-in-multiple regions-of-interest (ROIs) approach to cover a reasonably large area. To increase the speed of deep convolutional features extraction from multiple ROIs, we design an effective ROI mapping strategy. The proposed tracker also provides flexibility to couple with the more advanced correlation filter trackers. The DeepHKCF tracker performs exceptionally well with deep features set up in a synthetic hyperspectral video generated by the digital imaging and remote sensing image generation (DIRSIG) software. In addition, we generate a large, synthetic, single-channel data set using DIRSIG to perform vehicle classification in the wide-area motion imagery (WAMI) platform. This way, the high fidelity of the DIRSIG software is proven, and a large-scale aerial vehicle classification data set is released to support studies on vehicle detection and tracking in the WAMI platform.

A Simulation-Based Approach to Assess Subpixel Vegetation Structural Variation Impacts on Global Imaging Spectroscopy

Consistent and scalable estimation of vegetation structural parameters—essential to understanding forest ecosystems—is widely investigated through remote sensing imaging spectroscopy. NASA’s proposed spaceborne mission, the Hyperspectral Infrared Imager (HyspIRI), will measure spectral radiance from 380 to 2500 nm in 10-nm contiguous bands with a 60-m ground sample distance (GSD) and provide a global benchmark from which future changes can be assessed. The historic foci of spectrometers have been foliar/canopy biochemistry and species classification; however, given the relatively large GSD of a spaceborne instrument, there is uncertainty as to the effects of subpixel vegetation structure on observed radiance. This paper, therefore, evaluates the linkages between the within-pixel vegetation structure and imaging spectroscopy signals at the pixel level. We constructed a realistic virtual forest scene representing the National Ecological Observatory Network (NEON) Pacific Southwest domain site. Anticipated HyspIRI data (60-m GSD) for this site were then simulated using the physics-driven Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Both the models were first validated via comparison to overflow classic Airborne Visible/Infrared Imaging Spectrometer and NEON’s imaging spectrometer (NIS). Then, to assess the impact of within-pixel: 1) tree canopy cover (CC); 2) tree positioning; and 3) distribution on large-footprint HyspIRI signals, we generated the variations of the baseline virtual forest scene and measured the anticipated spectral radiance using DIRSIG. Results indicate that HyspIRI is sensitive to subpixel vegetation structural variation in the visible to a short-wavelength infrared spectrum due to vegetation structural changes. This has implications for improving the system’s suitability for consistent global vegetation structural assessments by adapting calibration strategies to account for this subpixel variation.

Modeling and Simulation of Deciduous Forest Canopy and Its Anisotropic Reflectance Properties Using the Digital Image and Remote Sensing Image Generation (DIRSIG) Tool

Extraction of biophysical information from forest canopies using temporal analysis of multispectral and hyperspectral data can be significantly improved by understanding its anisotropic reflectance properties. However, limitations on the accessibility and data collection techniques in the field reduce the availability of high-resolution bidirectional reflectance measurements (BRDF) to a few datasets. These limitations can be mitigated in a virtual environment and this paper presents an approach to model the spectral BRDF of a forest canopy using the Digital Image and Remote Sensing Image Generation (DIRSIG) tool. The three-dimensional geometries of the trees were modeled using forest inventory data and OnyxTree, while the spectral properties of the geometric elements were assigned based on the field collected spectra and PROSPECT inversion model. The DIRSIG tool was used as a virtual goniometer to measure the BRDF observations for varying sun-view geometries and a full hemispherical BRDF model was constructed by fitting the measurements to a semiempirical BRDF model. This paper discusses the methods involved in modeling the forest canopy scene, sensitivity of the radiative transfer, BRDF sampling and modeling strategies, model accuracy and its effect on real-world simulations. The model fit results indicate a root mean square error of less than 5% relative to the forests reflectance in the VIS-NIR-SWIR region. The simulated BRDF matched to within 2% of the Landsat-8 surface reflectance product in the red and NIR bands. The results can be used directly to evaluate BRDF modeling algorithms and the proposed method can be easily extended for other biomes.

Towards an Improved LAI Collection Protocol via Simulated and Field-Based PAR Sensing.

In support of NASA’s next-generation spectrometer—the Hyperspectral Infrared Imager (HyspIRI)—we are working towards assessing sub-pixel vegetation structure from imaging spectroscopy data. Of particular interest is Leaf Area Index (LAI), which is an informative, yet notoriously challenging parameter to efficiently measure in situ. While photosynthetically-active radiation (PAR) sensors have been validated for measuring crop LAI, there is limited literature on the efficacy of PAR-based LAI measurement in the forest environment. This study (i) validates PAR-based LAI measurement in forest environments, and (ii) proposes a suitable collection protocol, which balances efficiency with measurement variation, e.g., due to sun flecks and various-sized canopy gaps. A synthetic PAR sensor model was developed in the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and used to validate LAI measurement based on first-principles and explicitly-known leaf geometry. Simulated collection parameters were adjusted to empirically identify optimal collection protocols. These collection protocols were then validated in the field by correlating PAR-based LAI measurement to the normalized difference vegetation index (NDVI) extracted from the “classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) data ( was 0.61). The results indicate that our proposed collecting protocol is suitable for measuring the LAI of sparse forest (LAI < 3–5 ()).

Simulation of Image Performance Characteristics of the Landsat Data Continuity Mission (LDCM) Thermal Infrared Sensor (TIRS)

The next Landsat satellite, which is scheduled for launch in early 2013, will carry two instruments: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). Significant design changes over previous Landsat instruments have been made to these sensors to potentially enhance the quality of Landsat image data. TIRS, which is the focus of this study, is a dual-band instrument that uses a push-broom style architecture to collect data. To help understand the impact of design trades during instrument build, an effort was initiated to model TIRS imagery. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool was used to produce synthetic “on-orbit” TIRS data with detailed radiometric, geometric, and digital image characteristics. This work presents several studies that used DIRSIG simulated TIRS data to test the impact of engineering performance data on image quality in an effort to determine if the image data meet specifications or, in the event that they do not, to determine if the resulting image data are still acceptable.

Assessing the impact of spectral and polarimetric data fusion via simulation to support multimodal sensor system design requirements

A series of trade studies was carried out using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to assess how varying the spectral signal-to-noise ratio (SNR), spectral ground sample distance (GSD), or target spectrum affected the impact of spectral and polarimetric data fusion via the spectral polarimetric integration (SPI) algorithm for a notional multimodal sensor. When varying the SNR, the impact depended on the constraints placed on the sensor's tasking. When spectral GSD was varied, the benefit of incorporating polarimetric information increased as the GSD increased. However, a threshold GSD was identified beyond which no benefit was observed. Reducing the target/background spectral contrast by changing the target spectrum from a red vehicle to a green vehicle produced variations in the impact due to fusion, although the SPI algorithm produced a general increase in performance in both cases. The trade studies demonstrated that incorporating additional polarimetric information may enable suitable performance with a less capable multispectral sensor. Finally, the SPI decision fusion algorithm was shown to be robust across a range of scenarios possibly encountered in the multimodal sensor design process.

Defining a process to fuse polarimetric and spectral data for target detection and explore the trade space via simulation

A process is developed to assess the effect of fusing polarimetric and spectral sensing modalities for an urban target detection scenario through simulation with the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Two novel multimodal fusion algorithms are proposed--one for the pixel level, and another for the decision level. A synthetic urban scene is validated to ensure the presence of enough background clutter. The signal-to-clutter ratios (SCR) of both the simulated spectral and polarimetric data are calculated, and the synthetic spectral SCR is compared to data collected with the Compact Airborne Spectral Sensor (COMPASS). A qualitative examination of the polarimetric background clutter level is also described. The fusion algorithms' performances are evaluated at 355 different sun-target-sensor viewing geometries, and a method to quantify the increase in performance is described. Tasking conditions where target detection performance is enhanced are identified and the decision fusion algorithm is shown to outperform the pixel fusion algorithm. The utility of polarimetric information is shown to vary with the sun-target-sensor geometry, but data fusion consistently enhances spectral target detection performance when the sensor is located in the sun's specular reflection lobe.

Fusion of multiple image types for the creation of radiometrically-accurate synthetic scenes

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multi-spectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG's ability to generate scenes "on demand." To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, we have developed a comprehensive workflow aimed at reducing the man-in-the-loop requirements for many aspects of synthetic hyperspectral scene construction. Through a fusion of 3D lidar data with passive imagery, we have been able to partially-automate many of the required tasks in the creation of high-resolution urban DIRSIG scenes. This paper presents a description of these techniques.