Classifying Reflectance Targets under Ambient Light Conditions Using Passive Spectral Measurements

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near−infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient−light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two−step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%.

[1]  Simon Bennertz,et al.  Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging , 2014, Remote. Sens..

[2]  R. Sahoo,et al.  Hyperspectral Remote Sensing , 2013, Encyclopedia of Mathematical Geosciences.

[3]  Gabriele Moser,et al.  Extraction of Spectral Channels From Hyperspectral Images for Classification Purposes , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Marco Dubbini,et al.  Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images , 2015, Remote. Sens..

[6]  P. Zarco-Tejada,et al.  REMOTE SENSING OF VEGETATION FROM UAV PLATFORMS USING LIGHTWEIGHT MULTISPECTRAL AND THERMAL IMAGING SENSORS , 2009 .

[7]  Arko Lucieer,et al.  Simplified radiometric calibration for UAS-mounted multispectral sensor , 2018 .

[8]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[9]  Marcos J. Montes,et al.  Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique , 2004 .

[10]  Chuiqing Zeng,et al.  Fusion of Multispectral Imagery and Spectrometer Data in UAV Remote Sensing , 2017, Remote. Sens..

[11]  Andreas Burkart,et al.  Deploying four optical UAV-based sensors over grassland: challenges and limitations , 2015 .

[12]  Lei Tian,et al.  SOIL FERTILITY CHARACTERIZATION IN AGRICULTURAL FIELDS USING HYPERSPECTRAL REMOTE SENSING , 2005 .

[13]  John P. Fulton,et al.  An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..

[14]  Jesse B. Hoagg,et al.  Characteristics that make dynamic systems difficult for a human to control , 2016, 2016 American Control Conference (ACC).

[15]  Carlos Saito,et al.  Multispectral imaging system with UAV integration capabilities for crop analysis , 2017, 2017 First IEEE International Symposium of Geoscience and Remote Sensing (GRSS-CHILE).

[16]  Frédéric Baret,et al.  Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots , 2008, Sensors.

[17]  Michael P. Sama,et al.  A Method for Reflectance Index Wavelength Selection from Moisture-Controlled Soil and Crop Residue Samples , 2017 .

[18]  M. Morana,et al.  A refined empirical line approach for reflectance factor retrieval from Landsat-5 TM and Landsat-7 ETM + , 2001 .

[19]  L. M. Domenzain,et al.  Necessary steps for the systematic calibration of a multispectral imaging system to achieve a targetless workflow in reflectance estimation: a study of Parrot SEQUOIA for precision agriculture , 2018, Defense + Security.

[20]  Chunhua Zhang,et al.  The application of small unmanned aerial systems for precision agriculture: a review , 2012, Precision Agriculture.

[21]  Gary E. Varvel,et al.  Use of Remote-Sensing Imagery to Estimate Corn Grain Yield , 2001 .

[22]  A. Burkart,et al.  A Novel UAV-Based Ultra-Light Weight Spectrometer for Field Spectroscopy , 2014, IEEE Sensors Journal.

[23]  Michael P. Sama,et al.  Moisture Content Classification of Soil and Stalk Residue Samples from Spectral Data Using Machine Learning , 2019 .

[24]  W. Verhoef,et al.  Modeling the impact of spectral sensor configurations on the FLD retrieval accuracy of sun-induced chlorophyll fluorescence , 2011 .

[25]  Michael P. Sama,et al.  A low-cost method for collecting hyperspectral measurements from a small unmanned aircraft system , 2018, Commercial + Scientific Sensing and Imaging.

[26]  K. Karantzalos,et al.  LEAF AREA INDEX ESTIMATION IN VINEYARDS FROM UAV HYPERSPECTRAL DATA, 2D IMAGE MOSAICS AND 3D CANOPY SURFACE MODELS , 2015 .

[27]  Joshua D Rudd,et al.  Application of satellite, unmanned aircraft system, and ground-based sensor data for precision agriculture: a review , 2017 .

[28]  Jesse B. Hoagg,et al.  On the effects of changing reference command as humans learn to control dynamic systems , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[29]  C. Daughtry,et al.  Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status , 2005, Precision Agriculture.

[30]  Amin Abedini,et al.  An effective and efficient heuristic for no-wait flow shop production to minimize total completion time , 2017, Comput. Ind. Eng..

[31]  Lorenzo Bruzzone,et al.  A new search algorithm for feature selection in hyperspectral remote sensing images , 2001, IEEE Trans. Geosci. Remote. Sens..

[32]  Arko Lucieer,et al.  Empirical line calibration of WorldView-2 satellite imagery to reflectance data: using quadratic prediction equations , 2012 .

[33]  L. Plümer,et al.  Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance , 2010 .

[34]  Jeffrey E. Herrick,et al.  Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management , 2009 .

[35]  S. Ha,et al.  Machine learning approaches to coastal water quality monitoring using GOCI satellite data , 2014 .

[36]  R. Jenssen,et al.  1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .

[37]  Arko Lucieer,et al.  Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing , 2012, Remote. Sens..

[38]  Lutz Plümer,et al.  A review of advanced machine learning methods for the detection of biotic stress in precision crop protection , 2014, Precision Agriculture.

[39]  L. Guanter,et al.  Spectral calibration of hyperspectral imagery using atmospheric absorption features. , 2006, Applied optics.

[40]  Raul Morais,et al.  Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..

[41]  Ali HamidiSepehr.  CLASSIFYING SOIL MOISTURE CONTENT USING REFLECTANCE-BASED REMOTE SENSING , 2018 .