Browsing by Author "Logan, Riley D."

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Benthic river algae mapping using hyperspectral imagery from unoccupied aerial vehicles
(SPIE-Intl Soc Optical Eng, 2024-06) Logan, Riley D.; Shaw, Joseph A.
The increasing prevalence of nuisance benthic algal blooms in freshwater systems has led to water quality monitoring programs based on the presence and abundance of algae. Large blooms of the nuisance filamentous algae, Cladophora glomerata, have become common in the waters of the Upper Clark Fork River in western Montana. To aid in the understanding of algal growth dynamics, unoccupied aerial vehicle (UAV)-based hyperspectral images were gathered at three field sites along the length of the river throughout the growing season of 2021. Select regions within images covering the spectral range of 400 to 850 nm were labeled based on a combination of professional judgment and spectral profiles and used to train a random forest classifier to identify benthic algal growth across several classes, including benthic growth dominated by Cladophora (Clado), benthic growth dominated by growth forms other than Cladophora (non-Clado), and areas below a visually detectable threshold of benthic growth (bare substrate). After classification, images were stitched together to produce spatial distribution maps of each river reach while also calculating the average percent cover for each reach, achieving an accuracy of approximately 99% relative to manually labeled images. Results of this analysis showed strong variability across each reach, both temporally (up to 40%) and spatially (up to 46%), indicating that UAV-based imaging with high-spatial resolution could augment and therefore improve traditional measurement techniques that are spatially limited, such as spot sampling.
Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks
(2021) Morales, Giorgio; Sheppard, John W.; Logan, Riley D.; Shaw, Joseph A.
In recent years, Hyperspectral Imaging (HSI) has become a powerful source for reliable data in applications such as remote sensing, agriculture, and biomedicine. However, hyperspectral images are highly data-dense and often benefit from methods to reduce the number of spectral bands while retaining the most useful information for a specific application. We propose a novel band selection method to select a reduced set of wavelengths, obtained from an HSI system in the context of image classification. Our approach consists of two main steps: the first utilizes a filter-based approach to find relevant spectral bands based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. The second step applies a wrapper-based approach to select bands from the reduced set based on their information entropy values, and trains a compact Convolutional Neural Network (CNN) to evaluate the performance of the current selection. We present classification results obtained from our method and compare them to other feature selection methods on two hyperspectral image datasets. Additionally, we use the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager. We show that our method produces more suitable results for a multispectral sensor design.
Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection
(2021-09) Morales, Giorgio; Sheppard, John W.; Logan, Riley D.; Shaw, Joseph A.
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.
Hyperspectral imaging and machine learning for monitoring produce ripeness
(2020-04) Logan, Riley D.; Scherrer, Bryan; Senecal, Jacob; Walton, Neil S.; Peerlinck, Amy; Sheppard, John W.; Shaw, Joseph A.
Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process that uses a visible near-infrared (VNIR) hyperspectral imager from Resonon, Inc., coupled with machine learning algorithms to assess the ripeness of various pieces of produce. The images were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using RGB images, full spectrum hyperspectral images, and the genetic algorithm feature selection method. Results showed that the genetic algorithm-based feature selection method outperforms RGB images for all tested produce, outperforms hyperspectral imagery for bananas, and matches hyperspectral imagery performance for green peppers. This feature selection method is being used to develop a low-cost multi-spectral imager for use in monitoring produce in grocery stores.
Measuring the polarization response of a VNIR hyperspectral imager
(2020-04) Logan, Riley D.; Shaw, Joseph A.
As the applications of hyperspectral imaging rapidly diversify, the need for accurate radiometric calibration of these imaging systems is becoming increasingly important. When performing radiometric measurements, the polarization response of the imaging system can be of particular interest if the scene contains partially polarized objects. For example, when imaging a scene containing water, surface reflections from the water will be partially polarized, possibly affecting the response of the imaging system. In this paper, the polarization response of a Resonon, Inc. visible near-infrared (VNIR) hyperspectral imaging system is assessed across a spectral range of 400nm to 1000 nm, with a spectral resolution of 2.1 nm. Efforts are currently underway to correct for the observed polarization response of the imaging system.
Optical transmittance of 3D printing materials
(Optica Publishing Group, 2021-07) Hamp, Shannon M.; Logan, Riley D.; Shaw, Joseph A.
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.