Alignment of UAV-hyperspectral bands using keypoint descriptors in a spectrally complex environment

Banerjee, Bikram Pratap; Raval, Simit; Cullen, Patrick J.

doi:10.1080/2150704x.2018.1446564

Cited by 16 publications

(21 citation statements)

References 4 publications

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“…Data collected through the portable handheld FPI system caused minor spectral misalignments due to unavoidable handheld movement of the sensor and due to slight movement of the canopy due to wind. This happens as the data in the FPI sensor is acquired in a snapshot bandwise manner with a small delay and sensor movement [28]. The hyperspectral bands were aligned using a previously developed band alignment workflow described in [28].…”

Section: In-field Ground Based Hyperspectral Data Processingmentioning

confidence: 99%

“…This happens as the data in the FPI sensor is acquired in a snapshot bandwise manner with a small delay and sensor movement [28]. The hyperspectral bands were aligned using a previously developed band alignment workflow described in [28]. The data was first flat-field corrected using dark current removal and white calibration panel, then was converted to the reflectance measurements using previously computed calibration coefficients with integrating sphere [7].…”

Section: In-field Ground Based Hyperspectral Data Processingmentioning

confidence: 99%

“…In addition to the data driven PSO-MEAC tuned mode, another aerial survey was made with an indices based [7] wavelength selection approach, using the same UAV flight characteristic and sensor exposure configuration. A band stabilization workflow was adopted to co-register spatial shifts between bands in hypercubes, from both the aerial acquisition modes [28]. Further, the regular radiometric, mosaicking and geometric correction procedure for hypercubes were carried out [7].…”

Section: E Uav-hyperspectral Survey and Assessmentmentioning

confidence: 99%

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A Particle Swarm Optimization based Approach to Pre-tune Programmable Hyperspectral Sensors

Banerjee¹,

Raval²

2021

Preprint

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This article presents development of an innovative approach to identify spectrally significant wavelength bands, for a given environment, to tune hyperspectral sensor acquisition before UAV borne surveys. As several programmable hyperspectral sensors are now available, it is often a challenge to consider the suitable wavelengths of interest. Researchers often conduct a thorough field survey to identify the composition of target endmembers in an area to identify suitable wavelengths before UAV survey, which is difficult and cumbersome. Otherwise, the selection of wavelengths by trial-and-error is error-prone. <br>To our knowledge, this is the first time a technique for optimal hyperspectral band (or feature) selection has been proposed to pre-tune UAV-hyperspectral sensors before the survey. A metaheuristic evolutionary workflow using Particle Swarm Optimisation was used for this. The method is easy in the field and efficient to identify optimal bands before UAV-hyperspectral surveys.<br>

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Section: In-field Ground Based Hyperspectral Data Processingmentioning

confidence: 99%

Section: In-field Ground Based Hyperspectral Data Processingmentioning

confidence: 99%

Section: E Uav-hyperspectral Survey and Assessmentmentioning

confidence: 99%

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A Particle Swarm Optimization based Approach to Pre-tune Programmable Hyperspectral Sensors

Banerjee¹,

Raval²

2021

Preprint

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“…Our approach obtained an average BPE of 0.71 pixels. Despite being a result inferior to that obtained in the soybean dataset, our approach still obtained the best result when compared to the proposed by (YASIR, 2018) and (BANERJEE;RAVAL;CULLEN, 2018). The framework proposed by (BANERJEE; RAVAL; CULLEN, 2018) obtained a low performance, regardless of the method for extracting features.…”

Section: Cotton Datasetmentioning

confidence: 57%

UAV-Multispectral Sensed Data Band Co-Registration Framework

Dias¹

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“…The mentioned situation is due to the simple fact that current techniques are consolidated and robust in more stable image processing problems and with greater spatial coverage when compared to those obtained by UAV (BANERJEE;RAVAL;CULLEN, 2018;BERNI et al, 2009;HUNT et al, 2010). As mentioned, the objects of study of this work are multi-spectral images.…”

Section: Problem Definitionmentioning

confidence: 99%

Detection and correction of deformations in UAVs’ images using deep learning

Silva¹

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To God, for the privilege of life. To my mother, Iris de Fátima Furtado Pinto, who has always provided me with love and support, and never having tried so hard to achieve my dreams.To my advisors, Professor Dr. André Ricardo Backes and Professor Dr. Mauricio Cunha Escarpinati, for the friendship, commitment, opportunities, and teachings of this journey.To Professor Dr. João Fernando Mari for his friendship and contribution to my training.To Professor MSc. Pedro Moisés de Sousa for all the advice and teachings. To Professors Dr. Bruno Augusto Nassif Travençolo and Dr. Dalcimar Casanova who kindly accepted to participate in the examination board of this work.To colleague Jocival Dantas Dias Júnior for the support provided.To the Federal University of Uberlândia and the Federal University of Viçosa, Institutions that I have provided me with academic and professional growth.To Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brazil (CAPES) -for financial support. This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brazil (CAPES) -Finance Code 001. I would like to thank too the company Sensix Inovações em Drones Ltda for providing the images used in the experiments."We are just an advanced breed of monkeys on a minor planet of a very average star.But we can understand the Universe. That makes us something very special."

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Alignment of UAV-hyperspectral bands using keypoint descriptors in a spectrally complex environment

Cited by 16 publications

References 4 publications

A Particle Swarm Optimization based Approach to Pre-tune Programmable Hyperspectral Sensors

A Particle Swarm Optimization based Approach to Pre-tune Programmable Hyperspectral Sensors

UAV-Multispectral Sensed Data Band Co-Registration Framework

Detection and correction of deformations in UAVs’ images using deep learning

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