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Basal Stem Rot (BSR) disease is major threat to oil palm plantation caused by fungi Ganoderma boninensis sp in which the symptom visually noticeable only at the late stage of infection. The objective of this study is to detect presence of BSR disease by using airborne hyperspectral imaging. The study site was located at Kertam Oil Palm Plantation, Sabah, Malaysia covering 5,000 ha area. Hyperspectral...
Imaging Fourier-transform infrared (FTIR) spectroscopy is a powerful method for the passive remote detection and identification of vapor emanations and surface contaminations. In the Defense and Security context, imaging FTIR can be used for the remote surveillance of locations suspected of illicit product fabrications. DRDC Valcartier recently initiated the development and field-validation of the...
To enable traceability of imaging spectrometer data, the associated measurement uncertainties have to be provided reliably. Here, a new tool for a Monte-Carlo-type measurement uncertainty propagation for the uncertainties that originate from the spectrometer itself is described. For this, an instrument model of the imaging spectrometer ROSIS is used. Combined uncertainties are then derived for radiometrically...
In this paper, we propose a dictionary pruning technique for signal unmixing, which is a recent unmixing concept aimed at extracting the signal contribution of an individual class of endmembers to each observed pixel in a hyperspectral scene. We focus on a specific application in the area of plant production system monitoring. The pruning technique allows to infer the physical state of vegetation...
The SYSIPHE system is the state of the art airborne hyperspectral imaging system developed in European cooperation. With a unique wide spectral range and a fine spatial resolution, its aim is to validate and quantify the information potential of hyperspectral imaging in military, security and environment applications. The first section of the paper recalls the architecture of the project. The second...
Subpixel mapping is the process of spatially assigning pixel land cover fractions produced by soft classification on sub-sampled land cover maps in order to improve the latter's spatial resolution. In this paper, we present a method to increase the accuracy of subpixel mapping techniques. The method takes the land cover map of any subpixel mapping method as input and alters subpixel class labels according...
Hyperspectral imagery has been shown to be useful in monitoring and mapping coastal benthic habitats, yet many coastal areas remain challenging due to factors such as high turbidity and variability over fine spatial scales. The estuarine waters of Elkhorn Slough terminating in Monterey Bay, California present an excellent study site for testing the limits of hyperspectral imaging spectroscopy in a...
Multiple instance learning techniques (MIL) can operate when there is a lack of target information as is commonly encountered in remotely sensed hyperspectral imagery. However, MIL solutions are typically attained at the cost of increased computational complexity, e.g. the multiple instance relevance vector machine. This increase in computational burden can be avoided by embedding these so-called...
Spectral unmixing is a challenging, ill-posed, inverse problem that may result in infinitely many solutions, most of which are meaningless. Constraints must be added to guide the search process and narrow the space of possible solutions. Multiple sources of information can be used to find such constraints. In this paper, we introduce a semi-supervised context dependent hyperspectral unmixing algorithm,...
In this work the performance of spectral unmixing procedures applied on hyperspectral images of granular mixtures are compared. For that purpose we consider a laboratory image and synthetic images, the latter being created using a original algorithmic process while borrowing some aspects of the real data. The nonlinear effects of light multiple scattering within the mixture can be partially compensated...
In this paper, a Field Programmable Gate Array (FPGA) implementation of the LCE (Lossy Compression for ExoMars) algorithm is presented. This algorithm shows a good quality/compression ratio tradeoff for hyperspectral images, at the expenses of a higher complexity with respect to lossless algorithms. In order to deal with this complexity levels, high level synthesis (HLS) tools, such as Catapult C,...
Spectral unmixing of hyperspectral images consists of estimating pure material spectra with their corresponding proportions (or abundances). Recently, non-linear unmixing has received growing interest in the geoscience and remote sensing community. This article proposes a new non-linear unmixing approach using non-negative matrix factorization method that takes into account physical constraints on...
In this paper, a new classification strategy for remotely sensed hyperspectral image data is presented and discussed. The proposed approach adopts a decision fusion strategy which combines a well-established classifier, such as the support vector machine (SVM), with the information provided by extended multi-attribute morphological profiles (EMAPs). EMAPs provide a multilevel characterization of an...
Visible and near infrared optical constants are required to quantitatively determining mineral abundances from remote sensing data. Preliminary optical constants of a synthetic potassium jarosite, an important sulfate mineral on both Earth and Mars, are determined using a new MatLab encoded minimization routine based on Hapke's treatment of radiative transfer theory. The steps for determining these...
In this study, a subspace detection technique is developed using a hybrid approach which combines both feature extraction and feature selection for the task of hyperspectral image classification. The proposed approach applies Kernel Principal Component Analysis (KPCA) at the first step, then feature selection from the KPCA images is accomplished by combining the KPCA score with a Jeffries-Matusita...
This paper investigates the use of Minimum Noise Fraction (MNF) components to improve the spectral separability of two specific thematic classes in airborne hyperspectral imagery using Spectral Angle Mapper (SAM). Particularly, we compared trends on data distribution before and after MNF transform. Two different data sets recorded with the Multispectral Infrared Visible Imaging Spectrometer (MIVIS)...
Present study investigates the endmember extraction and classification of tropical trees (India) using Spectral Feature Fitting (SFF) & Spectral Angle Mapper (SAM) algorithms. Space-borne Hyperion data was acquired for two different dates (October, 2006 & January, 2011). Endmembers were picked up from highest percentage occupancy of a class. Classification was performed on a combination of...
In this paper, we propose an improved Expectation Maximization (EM) algorithm for hyperspectral image classification. As an excellent machine learning algorithm, EM is an iterative process for finding Maximum A Posteriori estimation (MAP) of parameters in Gaussian Mixture Models (GMMs). With the ability to deal with missing data, EM is considered excellent for solving the insufficient samples training...
Qinghai Lake, which is the largest salt water lake in China and a major obstacle in the burgeoning Eastward desertification from West China, has a vital role in the ecological sustainability of Tibetan Plateau. From the ecological perspective, the environment of Qinghai Lake became worse since the end of last century, while due to its remoteness, the field surveys in this area are difficult to carry...
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