UAV-Multispectral Sensed Data Band Co-Registration Framework

Precision farming has greatly benefited from new technologies over the years. The use of multispectral and hyperspectral sensors coupled to Unmanned Aerial Vehicles (UAV) has enabled farms to monitor crops, improve the use of resources and reduce costs. Despite being widely used, multispectral images present a natural misalignment among the various spectra due to the use of different sensors. The variation of the analyzed spectrum also leads to a loss of characteristics among the bands which hinders the feature detection process among the bands, which makes the alignment process complex. In this work, we propose a new framework for the band co-registration process based on two premises: i) the natural misalignment is an attribute of the camera, so it does not change during the acquisition process; ii) the speed of displacement of the UAV when compared to the speed between the acquisition of the first to the last band, is not sufficient to create significant distortions. We compared our results with the ground-truth generated by a specialist and with other methods present in the literature. The proposed framework had an average back-projection (BP) error of 0.425 pixels, this result being 335% better than the evaluated frameworks.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorDissertação (Mestrado)A agricultura de precisão se beneficiou muito das novas tecnologias ao longo dos anos. O uso de sensores multiespectrais e hiperespectrais acoplados aos Veículos Aéreos Não Tripulados (VANT) permitiu que as fazendas monitorassem as lavouras, melhorassem o uso de recursos e reduzissem os custos. Apesar de amplamente utilizadas, as imagens multiespectrais apresentam um desalinhamento natural entre os vários espectros devido ao uso de diferentes sensores. A variação do espectro analisado também leva à perda de características entre as bandas, o que dificulta o processo de detecção de atributos entre as bandas, o que torna complexo o processo de alinhamento. Neste trabalho, propomos um novo framework para o processo de alinhamento entre as bandas com base em duas premissas: i) o desalinhamento natural é um atributo da câmera, e por esse motivo ele não é alterado durante o processo de aquisição; ii) a velocidade de deslocamento do VANT, quando comparada à velocidade entre a aquisição da primeira e a última banda, não é suficiente para criar distorções significativas. Os resultados obtidos foram comparados com o padrão ouro gerado por um especialista e com outros métodos presentes na literatura. O framework proposto teve um back-projection error (BP) de 0, 425 pixels, sendo este resultado 335% melhor aos frameworks avaliados

NPP VIIRS Early On-Orbit Geometric Performance

The NASA/NOAA Visible Infrared Imager Radiometer Suite (VIIRS) instrument on-board the Suomi National Polar-orbiting Partnership (NPP) satellite was launched in October, 2011. The instrument geometric performance includes sensor spatial response, band-to-band co-registration (BBR), and geolocation accuracy and precision. The geometric performance is an important aspect of sensor data record (SDR) calibration and validation. In this paper we will discuss geometric performance parameter characterization using the first seven-month of VIIRS' earth and lunar data, and compare with the at-launch performance using ground testing data and analysis of numerical modeling results as the first step in on-orbit geometric calibration and validation

SNPP VIIRS Spectral Bands Co-Registration and Spatial Response Characterization

The Visible Infrared Imager Radiometer Suite (VIIRS) instrument onboard the Suomi National Polarorbiting Partnership (SNPP) satellite was launched on 28 October 2011. The VIIRS has 5 imagery spectral bands (I-bands), 16 moderate resolution spectral bands (M-bands) and a panchromatic day/night band (DNB). Performance of the VIIRS spatial response and band-to-band co-registration (BBR) was measured through intensive pre-launch tests. These measurements were made in the non-aggregated zones near the start (or end) of scan for the I-bands and M-bands and for a limited number of aggregation modes for the DNB in order to test requirement compliance. This paper presents results based on a recently re-processed pre-launch test data. Sensor (detector) spatial impulse responses in the scan direction are parameterized in terms of ground dynamic field of view (GDFOV), horizontal spatial resolution (HSR), modulation transfer function (MTF), ensquared energy (EE) and integrated out-of-pixel (IOOP) spatial response. Results are presented for the non-aggregation, 2-sample and 3-sample aggregation zones for the I-bands and M-bands, and for a limited number of aggregation modes for the DNB. On-orbit GDFOVs measured for the 5 I-bands in the scan direction using a straight bridge are also presented. Band-to-band co-registration (BBR) is quantified using the prelaunch measured band-to-band offsets. These offsets may be expressed as fractions of horizontal sampling intervals (HSIs), detector spatial response parameters GDFOV or HSR. BBR bases on HSIs in the non-aggregation, 2-sample and 3-sample aggregation zones are presented. BBR matrices based on scan direction GDFOV and HSR are compared to the BBR matrix based on HSI in the non-aggregation zone. We demonstrate that BBR based on GDFOV is a better representation of footprint overlap and so this definition should be used in BBR requirement specifications. We propose that HSR not be used as the primary image quality indicator, since we show that it is neither an adequate representation of the size of sensor spatial response nor an adequate measure of imaging quality

NPP VIIRS Geometric Performance Status

Visible Infrared Imager Radiometer Suite (VIIRS) instrument on-board the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP) satellite is scheduled for launch in October, 2011. It is to provide satellite measured radiance/reflectance data for both weather and climate applications. Along with radiometric calibration, geometric characterization and calibration of Sensor Data Records (SDRs) are crucial to the VIIRS Environmental Data Record (EDR) algorithms and products which are used in numerical weather prediction (NWP). The instrument geometric performance includes: 1) sensor (detector) spatial response, parameterized by the dynamic field of view (DFOV) in the scan direction and instantaneous FOV (IFOV) in the track direction, modulation transfer function (MTF) for the 17 moderate resolution bands (M-bands), and horizontal spatial resolution (HSR) for the five imagery bands (I-bands); 2) matrices of band-to-band co-registration (BBR) from the corresponding detectors in all band pairs; and 3) pointing knowledge and stability characteristics that includes scan plane tilt, scan rate and scan start position variations, and thermally induced variations in pointing with respect to orbital position. They have been calibrated and characterized through ground testing under ambient and thermal vacuum conditions, numerical modeling and analysis. This paper summarizes the results, which are in general compliance with specifications, along with anomaly investigations, and describes paths forward for characterizing on-orbit BBR and spatial response, and for improving instrument on-orbit performance in pointing and geolocation

Automated Image Registration And Mosaicking For Multi-Sensor Images Acquired By A Miniature Unmanned Aerial Vehicle Platform

Algorithms for automatic image registration and mosaicking are developed for a miniature Unmanned Aerial Vehicle (MINI-UAV) platform, assembled by Air-O-Space International (AOSI) L.L.C.. Three cameras onboard this MINI-UAV platform acquire images in a single frame simultaneously at green (550nm), red (650 nm), and near infrared (820nm) wavelengths, but with shifting and rotational misalignment. The area-based method is employed in the developed algorithms for control point detection, which is applicable when no prominent feature details are present in image scenes. Because the three images to be registered have different spectral characteristics, region of interest determination and control point selection are the two key steps that ensure the quality of control points. Affine transformation is adopted for spatial transformation, followed by bilinear interpolation for image resampling. Mosaicking is conducted between adjacent frames after three-band co-registration. Pre-introducing the rotation makes the area-based method feasible when the rotational misalignment cannot be ignored. The algorithms are tested on three image sets collected at Stennis Space Center, Greenwood, and Oswalt in Mississippi. Manual evaluation confirms the effectiveness of the developed algorithms. The codes are converted into a software package, which is executable under the Microsoft Windows environment of personal computer platforms without the requirement of MATLAB or other special software support for commercial-off-the-shelf (COTS) product. The near real-time decision-making support is achievable with final data after its installation into the ground control station. The final products are color-infrared (CIR) composite and normalized difference vegetation index (NDVI) images, which are used in agriculture, forestry, and environmental monitoring

Service robotics and machine learning for close-range remote sensing

L'abstract è presente nell'allegato / the abstract is in the attachmen

Suomi NPP VIIRS Prelaunch and On-orbit Geometric Calibration and Characterization

The Visible Infrared Imager Radiometer Suite (VIIRS) sensor was launched 28 October 2011 on the Suomi National Polarorbiting Partnership (SNPP) satellite. VIIRS has 22 spectral bands covering the spectrum between 0.412 m and 12.01 m, including 16 moderate resolution bands (M-bands) with a spatial resolution of 750 m at nadir, 5 imaging resolution bands (I-bands) with a spatial resolution of 375 m at nadir, and 1 day-night band (DNB) with a near-constant 750 m spatial resolution throughout the scan. These bands are located in a visible and near infrared (VisNIR) focal plane assembly (FPA), a short- and mid-wave infrared (SWMWIR) FPA and a long-wave infrared (LWIR) FPA. All bands, except the DNB, are co-registered for proper environmental data records (EDRs) retrievals. Observations from VIIRS instrument provide long-term measurements of biogeophysical variables for climate research and polar satellite data stream for the operational communitys use in weather forecasting and disaster relief and other applications. Well Earth-located (geolocated) instrument data is important to retrieving accurate biogeophysical variables. This paper describes prelaunch pointing and alignment measurements, and the two sets of on-orbit correction of geolocation errors, the first of which corrected error from 1,300 m to within 75 m (20 I-band pixel size), and the second of which fine tuned scan angle dependent errors, bringing VIIRS geolocation products to high maturity in one and a half years of the SNPP VIIRS on-orbit operations. Prelaunch calibration and the on-orbit characterization of sensor spatial impulse responses and band-to-band co-registration (BBR) are also described