Search results for "Multispectral image"
showing 10 items of 192 documents
Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression
2021
The growth of rice is a sequence of three different phenological phases. This sequence of change in rice phenology implies that the condition of the plant during the vegetative phase relates directly to the health of leaves functioning during the reproductive and ripening phases. As such, accurate monitoring is important towards understanding rice growth dynamics. Leaf Area Index (LAI) is an important indicator of rice yields and the availability of this information during key phenological phases can support more informed farming decisions. Satellite remote sensing has been adopted as a proxy to field measurements of LAI and with the launch of freely available high resolution Satellite imag…
A Database of Spectral Filter Array Images that Combine Visible and NIR
2017
International audience; Spectral filter array emerges as a multispectral imaging technology, which could benefit several applications. Although several instantiations are prototyped and commercialized, there are yet only a few raw data available that could serve research and help to evaluate and design adequate related image processing and algorithms. This document presents a freely available spectral filter array database of images that combine visible and near infra-red information.
Regularized multiresolution spatial unmixing for ENVISAT/MERIS and landsat/TM image fusion
2011
Earth observation satellites currently provide a large volume of images at different scales. Most of these satellites provide global coverage with a revisit time that usually depends on the instrument characteristics and performance. Typically, medium-spatial-resolution instruments provide better spectral and temporal resolutions than mapping-oriented high-spatial-resolution multispectral sensors. However, in order to monitor a given area of interest, users demand images with the best resolution available, which cannot be reached using a single sensor. In this context, image fusion may be effective to merge information from different data sources. In this letter, an image fusion approach ba…
A geostatistical approach to map near-surface soil moisture through hyperspatial resolution thermal inertia.
2021
Thermal inertia has been applied to map soil water content exploiting remote sensing data in the short and long wave regions of the electromagnetic spectrum. Over the last years, optical and thermal cameras were sufficiently miniaturized to be loaded onboard of unmanned aerial systems (UASs), which provide unprecedented potentials to derive hyperspatial resolution thermal inertia for soil water content mapping. In this study, we apply a simplification of thermal inertia, the apparent thermal inertia (ATI), over pixels where underlying thermal inertia hypotheses are fulfilled (unshaded bare soil). Then, a kriging algorithm is used to spatialize the ATI to get a soil water content map. The pr…
Monitoring Coastal Lagoon Water Quality Through Remote Sensing: The Mar Menor as a Case Study
2019
The Mar Menor is a hypersaline coastal lagoon located in the southeast of Spain. This fragile ecosystem is suffering several human pressures, such as nutrient and sediment inputs from agriculture and other activities and decreases in salinity. Therefore, the development of an operational system to monitor its evolution is crucial to know the cause-effect relationships and preserve the natural system. The evolution and variability of the turbidity and chlorophyll-a levels in the Mar Menor water body were studied here through the joint use of remote sensing techniques and in situ data. The research was undertaken using Operational Land Imager (OLI) images on Landsat 8 and two SPOT images, bec…
Assessment of workflow feature selection on forest LAI prediction with sentinel-2A MSI, landsat 7 ETM+ and Landsat 8 OLI
2020
The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of…
Identification of the most informative wavelengths for non-invasive melanoma diagnostics in spectral region from 450 to 950 nm
2020
In this study 300 skin lesion (including 32 skin melanomas) multispectral data cubes were analyzed. The multi-step and single step machine learning approaches were analyzed to find the wavebands that provide the most information that helps discriminate skin melanoma from other benign pigmented lesions. The multi-step machine learning approach assumed training several models but proved itself to be ineffective. The reason for that is a necessity to train a segmentation model on a very small dataset and utilization of standard machine learning classifier which have shown poor classification performance. The single-step approach is based on a deep learning neural network. We have conducted 260…
Dynamic best spectral bands selection for face recognition
2014
In this paper, face recognition in uncontrolled illumination conditions is investigated. A twofold contribution is proposed. First, three state-of-art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are evaluated upon the IRIS-M3 face database to study their robustness against a high illumination variation conditions. Second, we propose to use visible multispectral images, provided by the same face database, to enhance the performance of the three mentioned algorithms. To reduce the high data dimensionality introduced by the use of multispectral images, we have designed a system t…
Convolutional Neural Networks for Multispectral Image Cloud Masking
2020
Convolutional neural networks (CNN) have proven to be state of the art methods for many image classification tasks and their use is rapidly increasing in remote sensing problems. One of their major strengths is that, when enough data is available, CNN perform an end-to-end learning without the need of custom feature extraction methods. In this work, we study the use of different CNN architectures for cloud masking of Proba-V multispectral images. We compare such methods with the more classical machine learning approach based on feature extraction plus supervised classification. Experimental results suggest that CNN are a promising alternative for solving cloud masking problems.
Improved Temperature and Emissivity Separation Algorithm for Multispectral and Hyperspectral Sensors
2017
The Temperature and Emissivity Separation (TES) algorithm was originally developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). This paper focuses on improving the TES algorithm. The main modification is the replacement of the normalized emissivity module with a new module, which is based on the smoothing of spectral radiance signatures. Smoothing is performed by estimating emissivity using an optimized approximation of the relationship between brightness temperature and emissivity. The improved TES algorithm, which is called Optimized Smoothing for Temperature Emissivity Separation (OSTES), was first tested on simulated data from three different sensors, …