An ultra-compact hyperspectral imaging system for use with an unmanned aerial vehicle with smartphone-sensor technology

Wilcox, Christopher C.; Montes, Marcos J.; Yetzbacher, Michael K.; Edelberg, Jason; Schlupf, Joseph A.

doi:10.1117/12.2303914

Cited by 5 publications

(11 citation statements)

References 2 publications

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“…However, the development of UAV platforms over the last decade has enabled the development of an intermediary protocol, in the form of UAV integrated hyperspectral sensing [8,11,20,33]. These UAV based platforms provide greater flexibility than traditional sensing methods, permitting the user to vary parameters such as survey size and flight altitude [12,26], in a manner tailored to the proposed application. Additionally, due to their typically small size and low weight they can be easily, and readily, stored and deployed [33,34].…”

Section: Technological Developments and Associated Complexitiesmentioning

confidence: 99%

Section: Technological Developments and Associated Complexitiesmentioning

confidence: 99%

“…The demand for smaller and lighter hyperspectral imaging sensors continues to grow, with the application of UAV integrated sensors being one of the most rapidly developing areas of remote sensing technology [8,12]. The desire to reduce the physical size of these sensor systems whilst maintaining the data quality available from larger units is an aspiration in both aerial and ground-based sensing configurations [12,37]. With the advent of widely available 3D printing services [38,39], and the continued development of sensors for both scientific and commercial purposes [11], the opportunities to pioneer units specifically tailored to desired application areas have never been greater.…”

Section: Technological Developments and Associated Complexitiesmentioning

confidence: 99%

“…However, there remain limitations related to the weight and power supply of these devices, with heavier payloads having a negative effect on the potential duration of aerial surveys [15]. Whilst this is limiting the practical utilization of these devices at present, as technologies continue to be miniaturized and UAVs themselves advance, survey flight times will become proportionately longer in the future [12].…”

Section: Applications Within Environmental Monitoringmentioning

confidence: 99%

“…Initially developed for remote sensing applications [4,11], hyperspectral imaging sensors typically acquire images across hundreds of narrow spectral bands within the visible, Near Infrared (NIR), and Mid Infrared (MIR) segments of the electromagnetic spectrum [3,11]. This enables the construction of an almost continuous reflectance spectrum for each pixel in a scene which, in turn, allows for the in-depth spectral examination of scene features that would be rather less perceptible with the coarser bandwidths of multispectral scanners [1,7,8,12]. This recent development in sensor technologies has led to the uptake of hyperspectral imaging methods across a wide variety of disciplines, opening new possibilities for measuring and monitoring multiple aspects of our environment [8].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems

Stuart

McGonigle

Willmott

2019

Sensors

233

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The development and uptake of field deployable hyperspectral imaging systems within environmental monitoring represents an exciting and innovative development that could revolutionize a number of sensing applications in the coming decades. In this article we focus on the successful miniaturization and improved portability of hyperspectral sensors, covering their application both from aerial and ground-based platforms in a number of environmental application areas, highlighting in particular the recent implementation of low-cost consumer technology in this context. At present, these devices largely complement existing monitoring approaches, however, as technology continues to improve, these units are moving towards reaching a standard suitable for stand-alone monitoring in the not too distant future. As these low-cost and light-weight devices are already producing scientific grade results, they now have the potential to significantly improve accessibility to hyperspectral monitoring technology, as well as vastly proliferating acquisition of such datasets.

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Section: Technological Developments and Associated Complexitiesmentioning

confidence: 99%

Section: Technological Developments and Associated Complexitiesmentioning

confidence: 99%

Section: Technological Developments and Associated Complexitiesmentioning

confidence: 99%

Section: Applications Within Environmental Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems

Stuart

McGonigle

Willmott

2019

Sensors

233

View full text Add to dashboard Cite

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Toward Robust River Plastic Detection: Combining Lab and Field‐Based Hyperspectral Imagery

Tasseron

Schreyers

Peller

et al. 2022

Earth and Space Science

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Plastic pollution in aquatic ecosystems has increased dramatically in the last five decades, with strong impacts on human and aquatic life. Recent studies endorse the need for innovative approaches to monitor the presence, abundance, and types of plastic in these ecosystems. One approach gaining rapid traction is the use of multi‐ and hyperspectral cameras. However, most experiments using this approach were in controlled environments, making findings challenging to apply in natural environments. We present a method linking lab‐ and field‐based identification of macroplastics using hyperspectral data (1,150–1,675 nm). Experiments using riverbank‐harvested macroplastics were set up in a laboratory environment, and on the banks of the Rhine River. Representative pixel selections of eleven lab‐based images (n = 786,264 pixels) and two field‐based images (n = 40,289 pixels) were used to analyze the differences between these environments. Next, classifier algorithms such as support vector machines (SVM), spectral angle mapper (SAM) and spectral information divergence (SID) were applied, because of their robustness to varying light conditions and high accuracies in mapping spectral similarities. Our results showed that SAM classifiers are most robust in separating plastic pixels from background elements. By applying lab‐based data for plastic detection in field‐based images, user accuracies for plastics to up to 93.6% (n = 8,370 plastic pixels) were attained. This study provides key fundamental insights in linking lab‐based data to plastic detection in the field. With this paper we aim to contribute to the development of future spectral missions to detect and monitor plastic pollution in aquatic ecosystems.

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Riverbank plastic distributions and how to sample them

Tasseron,

Emmerik,

de Winter

et al. 2024

Preprint

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As plastic pollution exists in aquatic ecosystems globally, monitoring its abundance and distribution has become crucial for understanding transport pathways, sources, sinks, and impacts. Riverbanks are accumulation zones for plastic, but the selection of monitoring methods is constrained by research goals, available resources, and site-specific conditions. This diversity in approaches has led to disparate datasets, highlighting the need for standardized monitoring protocols. Here, we study the spatial distribution of plastic at the riverbank scale, quantify the uncertainty of existing riverbank methods, and provide recommendations for improved monitoring based on the balance between uncertainty loss and increase in effort. We measured riverbank plastic abundance at eight Dutch riverbanks, categorizing the items using 108 item categories (River-OSPAR). For every riverbank, an area of 100 by 25 meters was subdivided into five-by-five-meter squares, resulting in 100 individual monitored sub-areas. We found riverbank plastic exhibited high spatial variability, with deposition patterns ranging from parallel to the waterline to clustered, random, or uniform (Moran's I between -0.050 and 0.301). Individual measurements from diverse sampling protocols are 5-49 times less accurate than estimates derived from extensive sampling, highlighting the diminishing impact of specific methods with increased data collection. Lastly, our findings suggest that increasing the sampling area quickly reaches diminishing returns in terms of accuracy. Reducing the sampled area by 80% only increases the uncertainty in estimating the true plastic density by 20%. While standardized protocols are essential for data comparability, a rigid, uniform sampling approach may be less efficient and resource-intensive than a flexible (step-wise) strategy that adapts to local conditions. By demonstrating that extensive sampling can mitigate the differences between unique sampling protocols, this study promotes a shift towards flexible and efficient riverbank plastic monitoring, ultimately accelerating global efforts to combat plastic pollution.

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An ultra-compact hyperspectral imaging system for use with an unmanned aerial vehicle with smartphone-sensor technology

Cited by 5 publications

References 2 publications

Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems

Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems

Toward Robust River Plastic Detection: Combining Lab and Field‐Based Hyperspectral Imagery

Riverbank plastic distributions and how to sample them

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