Search results for " SENSING"
showing 10 items of 1517 documents
A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion
2015
article i nfo The focus of the current study is to compare data fusion methods applied to sensors with medium- and high- spatial resolutions. Two documented methods are applied, the spatial and temporal adaptive reflectance fusion model (STARFM) and an unmixing-based method which proposes a Bayesian formulation to incorporate prior spectral information.Furthermore, thestrengths of both algorithms arecombined ina novel data fusionmethod: the Spatial and Temporal Reflectance Unmixing Model (STRUM). The potential of each method is demonstrated using simulation imagery and Landsat and MODIS imagery. The theoretical basis of the algorithms causes STARFM and STRUM to produce Landsat-like reflecta…
Snapshot hyperspectral system for noninvasive skin blood oxygen saturation monitoring
2018
The present study introduces recently developed compact hyperspectral snapshot system (device and software) for skin oxygen saturation monitoring. This prototype device involves compact snapshot hyperspectral camera, multi-wavelength illuminator, optical filter and crossed polarizers. The device was validated using reference color samples and and in-vivo during finger arterial occlusion tests. The prototype system demonstrated good performance of skin hyperspectral measurements in spectral range of 500-630nm. The results confirmed reliability of developed system for in-vivo assessment of skin blood oxygen saturation.
Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability
2020
Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensively. Applications on classification and parameter retrieval are making a difference: methods are very accurate, can handle large amounts of data, and can deal with spatial and temporal data structures efficiently. Nevertheless, several important challenges need still to be addressed. First, current standard deep architectures cannot deal with long-range dependencies so distant driving processes (in space or time) are not captured, and the…
Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection
2008
The multitemporal classification of remote sensing images is a challenging problem, in which the efficient combination of different sources of information (e.g., temporal, contextual, or multisensor) can improve the results. In this paper, we present a general framework based on kernel methods for the integration of heterogeneous sources of information. Using the theoretical principles in this framework, three main contributions are presented. First, a novel family of kernel-based methods for multitemporal classification of remote sensing images is presented. The second contribution is the development of nonlinear kernel classifiers for the well-known difference and ratioing change detectio…
A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover
2014
Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, gras…
Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval
2013
Abstract ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we f…
Prelaunch assessment of worldview-3 information content
2014
The upcoming WorldView-3 satellite is designed to collect unique data by combining very-high spatial resolution (VHR) with observation bands in the short wave infrared (SWIR) in addition to the visible and near-infrared (VNIR) multispectral and panchromatic bands currently available on the VHR WorldView-2 system. These SWIR bands were specifically selected in order to target unique reflectance and absorption features presented by various surface materials and should, therefore, significantly improve the platforms information content for many image mining applications. This presentation explores the information content available to the WorldView-3 platform in two ways. First, second-order st…
Structured Output SVM for Remote Sensing Image Classification
2011
Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…
FPGA/LST-SW Encryption Module Implementation for Satellite Remote Sensing Secure Systems
2020
The need for security of data transmitted from satellites to the ground has increased. Therefore, the need for secure onboard systems is in great demand, particularly in satellite remote sensing missions. This paper describes an approach for a secure Field Programmable Gate Arrays (FPGA) implementation of the Land Surface Temperature Split Window (LST-SW) algorithm, with objective to meat real-time requirements, area optimization and achieved higher Throughput goals to be sufficient for a secure remote sensing satellite applications and missions. The system is designed using VHDL (VHSIC Hardware Description Language) in a Highlevel design method. The experimental results demonstrate that th…
Multi-beam capabilities for high precision astrometry at low frequencies using VLBI
2011
We are carrying out a simulation study to characterise the advantages of VLBI with multiple beams, which will be a feature of the next generation of instruments. We will focus on VLBI astrometric measurements at lower frequencies (1.4 GHz and below). For our simulations, we have selected a network consisting of ASKAP, the Australian SKA precursor, plus existing Australian antennas from the LBA (Long Baseline Array) and the new antenna in New Zealand (figure 1a). We have used different models to represent the ionospheric turbulences and frequencies. The preliminary results show an improvement of an order of magnitude in the astrometric precision achieved using multiple calibrators with angul…