Farid Qamar

Background: Vegetation spectral reflectance obtained with hyperspectral imaging (HSI) offer non-invasive means for the non-destructive study of their physiological status. The light intensity at visible and near-infrared wavelengths (VNIR, 0.4–1.0µm) captured by the sensor are composed of mixtures of spectral components that include the vegetation reflectance, atmospheric attenuation, top-of-atmosphere solar irradiance, and sensor artifacts. Common methods for the extraction of spectral reflectance from the at-sensor spectral radiance offer a trade-off between explicit knowledge of atmospheric conditions and concentrations, computational efficiency, and prediction accuracy, and are generally geared towards nadir pointing platforms. Therefore, a method is needed for the accurate extraction of vegetation reflectance from spectral radiance captured by ground-based remote sensors with a side-facing orientation towards the target, and a lack of knowledge of the atmospheric parameters.Results: We propose a framework for obtaining the vegetation spectral reflectance from at-sensor spectral radiance, which relies on a time-dependent Encoder-Decoder Convolutional Neural Network trained and tested using simulated spectra generated from radiative transfer modeling. Simulated at-sensor spectral radiance are produced from combining 1440 unique simulated solar angles and atmospheric absorption profiles, and 1000 different spectral reflectance curves of vegetation with various health indicator values, together with sensor artifacts. Creating an ensemble of 10 models, each trained and tested on a separate 10% of the dataset, results in the prediction of the vegetation spectral reflectance with a testing r²of 98.1% (±0.4). This method produces consistently high performance with accuracies >90% for spectra with resolutions as low as 40 channels in VNIR each with 40 nm full width at half maximum (FWHM) and greater, and remains viable with accuracies >80% down to a resolution of 10 channels with 60 nm FWHM. When applied to real sensor obtained spectral radiance data, the predicted spectral reflectance curves showed general agreement and consistency with those corrected by the Compound Ratio method.Conclusions: We propose a method that allows for the accurate estimation of the vegetation spectral reflectance from ground-based HSI platforms with sufficient spectral resolution. It is capable of extracting the vegetation spectral reflectance at high accuracy in the absence of knowledge of the exact atmospheric compositions and conditions at time of capture, and the lack of available sensor-measured spectral radiance and their true ground-truth spectral reflectance profiles.

This paper presents a novel framework for estimating the covariance and uncertainties of atmospheric parameters and the reflectance spectra of urban surfaces in high-resolution ground-based hyperspectral images. By integrating open source software for atmospheric modeling and statistical sampling, we demonstrate the use of Markov Chain Monte Carlo (MCMC) sampling to quantify the full posterior distributions in a joint fit of both molecular concentrations for atmospheric attenuation and parameterized surface reflectance to spectroscopic observations of an urban scene. We present a use case at visible and near-infrared wavelengths (0.4-1.0 micron) in ∼850 spectral channels where the uncertainty in atmospherically corrected surface reflectance of vegetation in the scene is acquired by propagating the uncertainties obtained from modeling the reflectance of a nearby building surface.

Using ground-based, remote hyperspectral images from 0.4–1.0 micron in ∼850 spectral channels—acquired with the Urban Observatory facility in New York City—we evaluate the use of one-dimensional Convolutional Neural Networks (CNNs) for pixel-level classification and segmentation of built and natural materials in urban environments. We find that a multi-class model trained on hand-labeled pixels containing Sky, Clouds, Vegetation, Water, Building facades, Windows, Roads, Cars, and Metal structures yields an accuracy of 90–97% for three different scenes. We assess the transferability of this model by training on one scene and testing to another with significantly different illumination conditions and/or different content. This results in a significant (∼45%) decrease in the model precision and recall as does training on all scenes at once and testing on the individual scenes. These results suggest that while CNNs are powerful tools for pixel-level classification of very high-resolution spectral data of urban environments, retraining between scenes may be necessary. Furthermore, we test the dependence of the model on several instrument- and data-specific parameters including reduced spectral resolution (down to 15 spectral channels) and number of available training instances. The results are strongly class-dependent; however, we find that the classification of natural materials is particularly robust, especially the Vegetation class with a precision and recall >94% for all scenes and model transfers and >90% with only a single training instance.

We describe a statistical approach for measuring the influence that a galaxy's closest companion has on the galaxy's properties out to arbitrarily wide separations. We begin by identifying the closest companion for every galaxy in a large spectroscopic sample of Sloan Digital Sky Survey galaxies. We then characterize the local environment of each galaxy by using the number of galaxies within 2 Mpc and by determining the isolation of the galaxy pair from other neighbouring galaxies. We introduce a sophisticated algorithm for creating a statistical control sample for each galaxy, matching on stellar mass, redshift, local density and isolation. Unlike traditional studies of close galaxy pairs, this approach is effective in a wide range of environments, regardless of how faraway the closest companion is (although a very distant closest companion is unlikely to have a measurable influence on the galaxy in question). We apply this methodology to measurements of galaxy asymmetry, and find that the presence of nearby companions drives a clear enhancement in galaxy asymmetries. The asymmetry excess peaks at the smallest projected separations (<10 kpc), where the mean asymmetry is enhanced by a factor of 2.0 ± 0.2. Enhancements in mean asymmetry decline as pair separation increases, but remain statistically significant (1σ–2σ) out to projected separations of at least 50 kpc.

We use images derived from collapsing, turbulent molecular cloud simulations without sinks to explore the effects of finite image angular resolution and noise on the derived clump mass function. These effects randomly perturb the clump masses, producing a lognormal clump mass function with a Salpeter-like high mass end. We show that the characteristic break mass of the simulated clump mass functions changes with the angular resolution of the images in a way that is entirely consistent with the observations. We also present some cautionary tales regarding sink particles and highlight the need to ensure that sinks actually correspond to distinct collapsing objects. We test several popular numerical sink criteria in the literature and compare to converged, non-sink results.