Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral images (HSIs) come at the cost of increased storage requirements, increased computational time to process, and highly redundant data. Thus, dimensionality reduction techniques are necessary to decrease the number of spectral bands while retaining the most useful information. Our contribution is two-fold: First, we propose a filter-based method called interband redundancy analysis (IBRA) based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. Second, we apply a wrapper-based approach called greedy spectral selection (GSS) to the results of IBRA to select bands based on their information entropy values and train a compact convolutional neural network to evaluate the performance of the current selection. We also propose a feature extraction framework that consists of two main steps: first, it reduces the total number of bands using IBRA; then, it can use any feature extraction method to obtain the desired number of feature channels. We present classification results obtained from our methods and compare them to other dimensionality reduction methods on three hyperspectral image datasets. Additionally, we used the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager.

Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of nitrogen from various sources. To improve understanding of bloom dynamics and how they affect water quality, often measured as algal biomass measured through pigment standing crops, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River in western Montana. Image data were collected across the spectral range of 400–1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chl a and phycocyanin standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to their growth form. Estimates of chl a and phycocyanin standing crops generated through a linear regression analysis are compared to in situ data, resulting in a maximum R2 of 0.96 for estimating fila/epip chl-a and 0.94 when estimating epiphytic phycocyanin. Estimates of pigment standing crops from total abundance, epiphytic, and the sum of filamentous and epiphytic sources are also included, resulting in a promising method for remotely estimating algal standing crops. This method addresses the shortcomings of current monitoring techniques, which are limited in spatial and temporal scale, by proposing a method for rapid collection of high-spatial-resolution pigment abundance estimates.

Harmful and nuisance algal blooms are becoming a greater concern to public health, riparian ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River in western Montana, USA, due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of contaminants through anthropogenic nitrogen enrichment along its banks. To improve understanding of bloom dynamics, such as algal biomass, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River. Image data were collected across the spectral range of 400 - 1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chlorophyll a standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to growth form. Estimates of total chlorophyll a standing crops generated through a brute-force analysis are compared to in-situ data, resulting in a maximum rsquared of 0.62 for estimating filamentous plus epiphytic chlorophyll a. Estimates of total and epilithic pigment standing crops are also included. The salient wavelengths bands used to estimate these pigments were then used as the basis for creating a low-cost imaging system for identifying algal blooms.

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The increasing prevalence of three-dimensional (3D) printing of optical housings and mounts necessitates a better understanding of the optical properties of printing materials. This paper describes a method for using multithickness samples of 3D printing materials to measure transmittance spectra at wavelengths from 400 to 2400 nm [visible to short-wave infrared (IR)]. In this method, 3D samples with material thicknesses of 1, 2, 3, and 4 mm were positioned in front of a uniform light source with a spectrometer probe on the opposing side to measure the light transmittance. Transmission depended primarily on the thickness and color of the sample, and multiple scattering prevented the use of a simple exponential model to relate transmittance, extinction, and thickness. A Solidworks file and a 3D printer file are included with the paper to enable measurements of additional materials with the same method.