Search results for "hyperspectral"
showing 10 items of 271 documents
Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images
2008
Hyperspectral remote sensing images are affected by different types of noise. In addition to typical random noise, nonperiodic partially deterministic disturbance patterns generally appear in the data. These patterns, which are intrinsic to the image formation process, are characterized by a high degree of spatial and spectral coherence. We present a new technique that faces the problem of removing the spatially coherent noise known as vertical striping, usually found in images acquired by push-broom sensors. The developed methodology is tested on data acquired by the Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-board Autonomy (PROBA) orbital platform, whi…
Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network
2020
In this study we develop a proof of concept of using generative adversarial neural networks in hyperspectral skin cancer imagery production. Generative adversarial neural network is a neural network, where two neural networks compete. The generator tries to produce data that is similar to the measured data, and the discriminator tries to correctly classify the data as fake or real. This is a reinforcement learning model, where both models get reinforcement based on their performance. In the training of the discriminator we use data measured from skin cancer patients. The aim for the study is to develop a generator for augmenting hyperspectral skin cancer imagery. peerReviewed
CEFLES2: The remote sensing component to quantify photosynthetic efficiency from the leaf to the region by measuring sun-induced fluorescence in the …
2009
The CEFLES2 campaign during the Carbo Europe Regional Experiment Strategy was designed to provide simultaneous airborne measurements of solar induced fluorescence and CO<sub>2</sub> fluxes. It was combined with extensive ground-based quantification of leaf- and canopy-level processes in support of ESA's Candidate Earth Explorer Mission of the "Fluorescence Explorer" (FLEX). The aim of this campaign was to test if fluorescence signal detected from an airborne platform can be used to improve estimates of plant mediated exchange on the mesoscale. Canopy fluorescence was quantified from four airborne platforms using a combination of novel sensors: (i) the prototype ai…
Hyperspectral Image Classification with Kernels
2007
The information contained in hyperspectral images allows the characterization, identification, and classification of land covers with improved accuracy and robustness. However, several critical problems should be considered in the classification of hyperspectral images, among which are (a) the high number of spectral channels, (b) the spatial variability of the spectral signature, (c) the high cost of true sample labeling, and (d) the quality of data. Recently, kernel methods have offered excellent results in this context. This chapter reviews the state-of-the-art hyperspectral image classifiers, presents two recently proposed kernel-based approaches, and systematically discusses the specif…
Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects
2012
Abstract Surface Urban Heat Island (SUHI) effect is defined as the increased surface temperature in urban areas in contrast to surrounding cooler temperatures in rural areas. In this paper, we study the characteristics that a spaceborne sensor must satisfy in terms of spatial resolution and overpass time to properly monitor the SUHI effect. For this, Land Surface Temperature (LST) maps, generated at different spatial resolution using the Airborne Hyperspectral Scanner (AHS) imagery, and in situ data of air temperature and LST obtained from the framework of the Dual-use European Security IR Experiment 2008 have been considered for the city of Madrid (Spain). The results showed that (1) spati…
Semi-Supervised Classification Method for Hyperspectral Remote Sensing Images
2004
A new approach to the classification of hyperspectral images is proposed. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning met…
Hyperspectral venous image quality assessment for optimum illumination range selection based on skin tone characteristics
2014
Background Subcutaneous veins localization is usually performed manually by medical staff to find suitable vein to insert catheter for medication delivery or blood sample function. The rule of thumb is to find large and straight enough vein for the medication to flow inside of the selected blood vessel without any obstruction. The problem of peripheral difficult venous access arises when patient’s veins are not visible due to any reason like dark skin tone, presence of hair, high body fat or dehydrated condition, etc. Methods To enhance the visibility of veins, near infrared imaging systems is used to assist medical staff in veins localization process. Optimum illumination is crucial to obt…
Lossless coding of hyperspectral images with principal polynomial analysis
2014
The transform in image coding aims to remove redundancy among data coefficients so that they can be independently coded, and to capture most of the image information in few coefficients. While the second goal ensures that discarding coefficients will not lead to large errors, the first goal ensures that simple (point-wise) coding schemes can be applied to the retained coefficients with optimal results. Principal Component Analysis (PCA) provides the best independence and data compaction for Gaussian sources. Yet, non-linear generalizations of PCA may provide better performance for more realistic non-Gaussian sources. Principal Polynomial Analysis (PPA) generalizes PCA by removing the non-li…
Software Framework for Hyperspectral Data Exploration and Processing in MATLAB
2017
This paper presents a user introduction and a general overview of the MATLAB software package hsicube developed by the author for simplifying the data manipulation and visualization tasks often encountered in hyperspectral analysis work, and the design principles and software development methods used by the author. The framework implements methods for slicing, masking, visualization and application of existing functions to hyperspectral data cubes without the need to use explicit indexing or reshaping, as well as enabling expressive syntax for combining these operations on the command line for highly efficient data analysis workflows. It also includes utilities for interfacing with existing…
In-scene atmospheric correction of hyperspectral thermal infrared images with nadir, horizontal, and oblique view angles
2012
Atmospheric corrections for hyperspectral thermal images acquired with nadir, horizontal, and oblique views have typically relied on atmospheric modelling software, such as Moderate Resolution Atmospheric Transmission MODTRAN, to estimate atmospheric parameters. Data-only corrections, which require only information from the scene, are more versatile and less labour intensive, but do not yet seem to have been applied to horizontal and oblique views. Here, we apply, and modify where necessary, one published data-only algorithm in-scene atmospheric correction ISAC to nadir, horizontal, and slanted views The Aerospace Corporation's Spatially Enhanced Broadband Array Spectrograph System SEBASS a…