The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Biomass is an important parameter that has a decisive influence on the final yield. Destructive measurements of biomass are time-consuming and labor-intensive. Proximal sensing methods using field spectrometers offer indirect observation and estimation of biomass. For this purpose, farmers' fields were investigated in a two-year growing season of rice and canopy reflectance was measured by spectrometers...
This paper presents a new factorization approach for hyperspectral data based on non-negativity constraints. The method does not assume a one to one correspondence between the pseudo-rank of the data matrix and the number of unique components present. Rather it assumes that the number of unique components is related to the number of extreme points of the cone formed by the data matrix. The cone is...
At 525 km in mean diameter, Vesta is the second largest object of the main asteroid belt [1, 2]. The Visible and InfraRed (VIR) spectrometer onboard Dawn provided more than 20 millions spectra at variable spatial resolution, allowing one to discriminate the different lithologies on Vesta [3, 4, 5]. In this work, we tested linear spectral unmixing on three different mixtures, to verify the applicability...
We present an airborne experiment on unmixing-based hyperspectral super-resolution using RGB imagery to examine the restoration of pure spectra comparing with ground-measured spectra and demonstrate its impact on target detection. An extended version of coupled nonnegative matrix factorization (CNMF) is used for hyperspectral super-resolution to deal with a challenging problem setting. Our experiment...
For hyperspectral image processing, dimensionality reduction is an important step, which has direct impact on hyperspectral image classification accuracy. Unsupervised band selection is an important means of data dimensionality reduction. This paper presents an ant colony optimization (ACO) algorithm based hyperspectral image band selection method (ACO-BS). First, four kinds of distance are used to...
Classification is an important and widely used technique for remotely sensed hyperspectral data interpretation. Although most techniques developed for classification assume that the spectral signatures provided by an imaging spectrometer can be interpreted as a unique and continuous signal, in practice this signal may be obtained after the combined individual responses from several different spectrometers...
Hyperspectral optical observations and the development of new processing strategies are key for a better understanding of complex marine ecosystems and space-time distribution of ecological parameters. In this paper, the methodologies to implement a simulator of hyperspectral-resolved optical data corresponding to highly dynamic marine environments are presented. The simulator is based on a coupled...
In this study two neural networks were implemented in order to emulate a retrieval model and to estimate the sulphur dioxide (SO2) columnar content and plume height from volcanic eruption. ANNs were trained using all IASI channels in TIR as inputs, and the corresponding values of SO2 content and height of plume obtained using the Oxford SO2 retrievals as target outputs. The retrieval is demonstrated...
Seepage of underground oil-gas reservoirs will bring stress effects for poisoning of roots of vegetation in the environment, which will be reflected in vegetation canopy reflectance spectra. In order to explore how to extract anomalous vegetation resulting from natural oil-gas microseepage by hyperspectral remote sensing images, this paper presents a data processing flow for effectively extracting...
Recent development in semiblind dictionary-aided hyperspectral unmixing (HU) shows that a classical method in sensor array processing, namely, multiple signal classification (MUSIC), provides an effective way for endmember identification. However, MUSIC (and in fact, other dictionary-based sparse regression algorithms) assumes that there are no mismatches between the true endmember signatures and...
Under sun-light illumination, the shape of the atmospheric oxygen bands (O2-B, 687 nm and O2-A, 760 nm) of the vegetation radiance is modified by chlorophyll fluorescence. However for far-range measurements, atmospheric effects also modify this shape. In this study, measurements in O2-A and O2-B absorption bands have been performed at different altitudes up to 3123 m over bare soil and wheat fields...
Sparse spectral unmixing can be modeled as a linear combination of endmembers contained in an overcomplete dictionary weighted by the corresponding sparse abundance vector. This method exploits the fact that there is only a small number of endmembers inside a pixel compared to the overcomplete endmember spectral dictionary. Since the information contained in hyperspectral pixels is often spatially...
To reduce huge consumption of processing hyperspectral images(HSI), a novel Bayesian unmixing compressive sensing framework is proposed to compress and reconstruct HSI effectively, called structured sparse Bayesian umixing compressive sensing(SSBUCS). SSBUCS unites compressive sensing and hyperspectral linear mixed model in Bayesian framework. An HSI is decomposed as a linear combination of endmembers...
In this paper an endmember constrained semi-supervised hyperspectral unmixing method is proposed. The linear model is used to represent the hyperspectral data. A priori information about the endmembers is incorporated into the objective function with soft regularization. This information can be acquired from a spectral library or from the data itself. Quantitative evaluation of the method is done...
Feature reduction of hyperspectral data is a big challenge, particularly because the reduced dimensions must preserve the separability properties and key information content. Nevertheless, various techniques have been developed so far and are well documented in the literature. Here we characterize a novel technique of feature reduction, with main emphasis on the ability of enhancing the informative...
Rare endmembers estimation is a very interesting and difficult issue in the unmixing field. We focus on the case of a rare endmember which appear as a change between two images of a same scene. We use both the information of the image where the new endmember is missing and change detection results to estimate the appearing endmember. We base the proposed approach on spectral unmixing with non negative...
This paper introduces the new scheme of the previously proposed Joinly Sparse Fusion of Hyperspectral and Multispectral Imagery (J-SparseFI-HM) fusion method. This extended, now fully automated and parallelized version of J-SparseFI-HM jointly estimates bundles of an adjustable number of high resolution hyperspectral bands by fusing corresponding low resolution bands with possibly multiple high resolution...
Classification of tree species is one of the most important applications in remote sensing. A methodology to classify tree species using hyperspectral and LiDAR data is proposed. The data processing consists of shadow correction, individual tree crown delineation, classification by support vector machine (SVM) and postprocessing by a smoothing filter. The authors applied this procedure to the data...
To acquire accurate, real-time hyperspectral images with high spatial resolution, we develop two types of low-cost, lightweight Whisk broom hyperspectral sensors that can be loaded onto lightweight unmanned autonomous vehicle (UAV) platforms. A system is composed of two Mini-Spectrometers, a polygon mirror, references for sensor calibration, a GPS sensor, a data logger and a power supply. The acquisition...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.