A comparison of spatial sampling techniques enabling first principles modeling of a synthetic aperture RADAR imaging platform

Gartley, Michael; Goodenough, Adam A.; Brown, Scott; Kauffman, Russel P.

doi:10.1117/12.849552

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…DIRSIG is a physics-based, first-principles radiometric modeling environment for the creation of synthetic remote sensing imagery that is radiometrically, geometrically, and temporally accurate [ 25 ]. The model is designed to generate passive broad-band, multispectral, imaging spectroscopy, low-light, polarized, active laser radar, and synthetic aperture radar datasets [ 26 , 27 , 28 ] through the integration of a suite of first-principles-based radiation propagation modules.…”

Section: Methodsmentioning

confidence: 99%

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

Yao

Kelbe

Leeuwen

et al. 2016

Sensors

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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 (R2 was 0.61). The results indicate that our proposed collecting protocol is suitable for measuring the LAI of sparse forest (LAI < 3–5 (normalm2/normalm2)).

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Section: Methodsmentioning

confidence: 99%

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

Yao

Kelbe

Leeuwen

et al. 2016

Sensors

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“…However, SAR images are difficult to acquire, understand, and process, resulting in limited datasets that meet the requirements [46]. Some computer simulation methods [47,48] have been implemented to directly calculate target SAR images using ray-tracing algorithms combined with the computer-aided design (CAD) models of the targets. This is due to the fact that high-frequency electromagnetic waves exhibit similar scattering characteristics to light.…”

Section: Sar Data Generationmentioning

confidence: 99%

CycleGAN-Based SAR-Optical Image Fusion for Target Recognition

Sun,

Yan,

2023

Remote Sensing

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The efficiency and accuracy of target recognition in synthetic aperture radar (SAR) imagery have seen significant progress lately, stemming from the encouraging advancements of automatic target recognition (ATR) technology based on deep learning. However, the development of a deep learning-based SAR ATR algorithm still faces two critical challenges: the difficulty of feature extraction caused by the unique nature of SAR imagery and the scarcity of datasets caused by the high acquisition cost. Due to its desirable image nature and extremely low acquisition cost, the simulated optical target imagery obtained through computer simulation is considered a valuable complement to SAR imagery. In this study, a CycleGAN-based SAR and simulated optical image fusion network (SOIF-CycleGAN) is designed and demonstrated to mitigate the adverse effects of both challenges simultaneously through SAR-optical image bidirectional translation. SAR-to-optical (S2O) image translation produces artificial optical images that are high-quality and rich in details, which are used as supplementary information for SAR images to assist ATR. Conversely, optical-to-SAR (O2S) image translation generates pattern-rich artificial SAR images and provides additional training data for SAR ATR algorithms. Meanwhile, a new dataset of SAR-optical image pairs containing eight different types of aircraft has been created for training and testing SOIF-CycleGAN. By combining image-quality assessment (IQA) methods and human vision, the evaluation verified that the proposed network possesses exceptional bidirectional translation capability. Finally, the results of the S2O and O2S image translations are simultaneously integrated into a SAR ATR network, resulting in an overall accuracy improvement of 6.33%. This demonstrates the effectiveness of SAR-optical image fusion in enhancing the performance of SAR ATR.

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“…10, 11 DIRSIG can produce passive single-band, multi-spectral or hyperspectral imagery from the visible through the thermal infrared regions of the electromagnetic spectrum. 12,13 In this study, DIRSIG was foremost used to simulate HyspIRI data; however, AVIRIS-C and NIS data were also simulated to ensure both radiometric and geometric consistencies between the virtual scenes and the real study sites. 14 Table 1 lists the key settings used for the simulation.…”

Section: Dirsig Simulationmentioning

confidence: 99%

Towards an improved understanding of the influence of subpixel vegetation structure on pixel-level spectra: a simulation approach

et al. 2016

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The planned NASA Hyperspectral Infrared Imager (HyspIRI) mission, equipped with an imaging spectrometer that has the capability of monitoring ecosystems globally, will provide an unprecedented opportunity to address scientific challenges related to ecosystem function and change. However, uncertainty remains around the impact of subpixel vegetation structure, in combination with the point spread function, on pixel-level imaging spectroscopy data. We estimated structural parameters, e.g., leaf area index (LAI), canopy cover, and tree location, from HyspIRI spectral data, with the goal of assessing how subpixel variation in these parameters impact pixel-level imaging spectroscopy data.The fine-scale variability of real vegetation structure makes this a challenging endeavor. Therefore, we utilized a simulation-based approach to counter the time-consuming and often destructive sampling needs of vegetation structural analysis and to simultaneously generate synthetic HyspIRI data pre-launch. Three virtual scenes were constructed, corresponding to the actual vegetation structure in the National Ecological Observatory Network's (NEON) Pacific Southwest Domain (Fresno, CA). These included an oak savanna, a dense coniferous forest, and a conifer-manzanita-mixed forest. Simulated spectroscopy data for these scenes were then generated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Simulations first were used to verify the physical model, virtual scene geometrical information, and simulation parameters. This was followed by simulations of HyspIRI data, where within-pixel structural variability was introduced, e.g., by iteratively changing per-pixel canopy cover and tree placement, tree clustering, leaf area index (LAI), etc., between simulation runs for the virtual scenes. Finally, narrow-band vegetation indices (VIs) were extracted from the data in an attempt to describe the variability of the subpixel structural parameters; this was done in order to assess VI robustness to changes in structural "levels", as well as placement of trees/canopies within the instrument's instantaneous field-of-view (IFOV). Our ultimate goal is not only to better understand how such subpixel variability influence imaging spectroscopy outputs, but also to better estimate vegetation structural parameters using spectra.We constructed regression models for LAI (R 2 = 0.92) and canopy cover (R 2 = 0.97) with narrow-band VIs via this simulation approach. Our models ultimately are intended to improve the HyspIRI mission's ability to monitor global vegetation structure.

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A comparison of spatial sampling techniques enabling first principles modeling of a synthetic aperture RADAR imaging platform

Cited by 8 publications

References 3 publications

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

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

CycleGAN-Based SAR-Optical Image Fusion for Target Recognition

Towards an improved understanding of the influence of subpixel vegetation structure on pixel-level spectra: a simulation approach

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