Search results for "Spectral image"
showing 10 items of 219 documents
Evaluation of the Colorimetric Performance of Single-Sensor Image Acquisition Systems Employing Colour and Multispectral Filter Array
2015
International audience; Single-sensor colour imaging systems mostly employ a colour filter array (CFA). This enables the acquisition of a colour image by a single sensor at one exposure at the cost of reduced spatial resolution. The idea of CFA fit itself well with multispectral purposes by incorporating more than three types of filters into the array which results in multispectral filter array (MSFA). In comparison with a CFA, an MSFA trades spatial resolution for spectral resolution. A simulation was performed to evaluate the colorimetric performance of such CFA/MSFA imaging systems and investigate the trade-off between spatial resolution and spectral resolution by comparing CFA and MSFA …
Validation of the Sentinel-3 Ocean and Land Colour Instrument (OLCI) Terrestrial Chlorophyll Index (OTCI): Synergetic Exploitation of the Sentinel-2 …
2018
Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI), and to ensure its utility in a wide range of operational applications, validation efforts are required. In the past, these activities have been constrained by the need for costly airborne hyperspectral data acquisition, but the Sentinel-2 Multispectral Instrument (MSI) now offers a promising alternative. In this paper, we explore the synergetic use of Sentinel-2 MSI data for validation of the Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valen…
Optimized Class-Separability in Hyperspectral Images
2016
International audience; Image visualization techniques are mostly based on three bands as RGB color composite channels for human eye to characterize the scene. This, however, is not effective in case of hyper-spectral images (HSI) because they contain dozens of informative spectral bands. To eliminate redundancy of spectral information among these bands, dimensionality reduction (DR) is applied while at the same trying to retain maximum information. In this paper, we propose a new method of information-preserved hyper-spectral satellite image visualization that is based on fusion of unsupervised band selection techniques and color matching function (CMF) stretching. The results show consist…
Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud
2020
Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution (30 m) observations over land. Our approach uses images from the Landsat (30 m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500 m spatial resolution and daily revisit cycle). We implem…
Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
2018
Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
2020
Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…
Efficient remote sensing image classification with Gaussian processes and Fourier features
2017
This paper presents an efficient methodology for approximating kernel functions in Gaussian process classification (GPC). Two models are introduced. We first include the standard random Fourier features (RFF) approximation into GPC, which largely improves the computational efficiency and permits large scale remote sensing data classification. In addition, we develop a novel approach which avoids randomly sampling a number of Fourier frequencies, and alternatively learns the optimal ones using a variational Bayes approach. The performance of the proposed methods is illustrated in complex problems of cloud detection from multispectral imagery.
Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north-easte…
2016
This study aims at evaluating the performance of the Maximum Entropy method in assessing landslide susceptibility, exploiting topographic and multispectral remote sensing predictors. We selected the catchment of the Giampilieri stream, which is located in the north-eastern sector of Sicily (southern Italy), as test site. On 1 October 2009, a storm rainfall triggered in this area hundreds of debris flow/avalanche phenomena causing extensive economical damage and loss of life. Within this area a presence-only-based statistical method was applied to obtain susceptibility models capable of distinguishing future activation sites of debris flow and debris slide, which where the main source of fai…
Inflight Radiometric Calibration of New Horizons' Multispectral Visible Imaging Camera (MVIC)
2017
© 2016 Elsevier Inc. We discuss two semi-independent calibration techniques used to determine the inflight radiometric calibration for the New Horizons’ Multi-spectral Visible Imaging Camera (MVIC). The first calibration technique compares the measured number of counts (DN) observed from a number of well calibrated stars to those predicted using the component-level calibration. The ratio of these values provides a multiplicative factor that allows a conversation between the preflight calibration to the more accurate inflight one, for each detector. The second calibration technique is a channel-wise relative radiometric calibration for MVIC's blue, near-infrared and methane color channels us…
Atmosphere-Space Interactions Monitor, Instrument and First Results
2019
The Atmosphere-Space Interaction Monitor (ASIM) is an observatory mounted outside the Columbus module on the International Space Station. It has been operational since April 13th, 2018. It contains two instruments: The Modular X- and Gamma-ray Sensor (MXGS) and The Modular Multispectral Imaging Array (MMIA). The objective of ASIM is to monitor thunderstorms and auroras, including lightning discharges, especially discharges upwards above thunderstorms. This paper presents the instrument package and some first results.