Hyperspectral Remote Sensing: Data Collection and Exploitation

Sandor-Leahy, S.; Shepanski, John

doi:10.1002/9780470027318.a2309

Cited by 2 publications

(2 citation statements)

References 33 publications

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“…Recent advances in remote sensing and machine learning can make a more scalable and low-cost solution possible. Hyperspectral remote sensing is an emerging field that uses imaging spectroscopy to obtain images in continuous spectral ranges from 0.4 to 14 µm [15]. Compared to its predecessor, multispectral imaging, hyperspectral imagery offers a variety of advantages, including high spatial resolution and an appreciably larger amount of spectral information [16].…”

Section: Introductionmentioning

confidence: 99%

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Agrawal¹,

Petersen

2021

Toxics

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Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R2=0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

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Section: Introductionmentioning

confidence: 99%

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Agrawal¹,

Petersen

2021

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“…For over two decades, hyperspectral imaging (HSI) sensors have demonstrated the capability to remotely detect subtle spectral features in reflected and emitted energy from the earth's surface and atmosphere. 1 However, the large volumes of data that are usually generated even from a few days of flight campaign make data processing and analysis extremely challenging. To find specific targets within the large hyperspectral data cubes in a timely manner would require an effective detection algorithm that minimizes the false alarm rate and the employment of powerful massively parallel processors such as those on a modern graphics processing unit (GPU).…”

Section: Introductionmentioning

confidence: 99%

Parallel algorithms for fast subpixel detection in hyperspectral imagery

2013

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We present parallel algorithms for fast subpixel detection of targets in hyperspectral imagery produced by our Hyperspectral Airborne Tactical Instrument (HATI-2500). The HATI-2500 hyperspectral imaging system has a blue-enhanced visible-near-IR (VNIR) and a full short-wave IR (SWIR) range response from 400 to 2500 nm. It has an industry-leading spectral resolution that ranges from 6 nm down to 1.5 nm in the VNIR region. The parallel detection algorithm selected for processing the hyperspectral data cubes is based on the adaptive coherence/cosine estimator (ACE). The ACE detector is a robust detector that is built upon the theory of generalized likelihood ratio testing (GLRT) in implementing the matched subspace detector to unknown parameters such as the noise covariance matrix. Subspace detectors involve projection transformations whose matrices can be efficiently manipulated through multithreaded massively parallel processors on modern graphics processing units (GPU). The GPU kernels developed in this work are based on the CUDA computing architecture. We constrain the detection problem to a model with known target spectral features and unstructured background. The processing includes the following steps: 1) scale and offset applied to convert the data from digital numbers to radiance values, 2) update the background inverse covariance estimate in a line-by-line manner, and 3) apply the ACE detector for each pixel for binary hypothesis testing. As expected, the algorithm is extremely effective for homogeneous background, such as open desert areas; and less effective in mixed spectral regions, such as those over urban areas. The processing rate is shown to be faster than the maximum frame rate of the camera (100 Hz) with a comfortable margin.

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Hyperspectral Remote Sensing: Data Collection and Exploitation

Cited by 2 publications

References 33 publications

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Parallel algorithms for fast subpixel detection in hyperspectral imagery

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