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Multitemporal Hyperspectral (HS) images can be used in Change Detection (CD) to identify and discriminate among different kinds of change due to the fine sampling of the spectrum by HS sensors. In this work we propose a novel method for unsupervised multiple CD in multitemporal HS data based on binary Spectral Change Vectors (SCVs) and an agglomerative hierarchical clustering. First, we perform binary...
Reference data (“ground truth”) maps are commonly used to quantitatively assess the performance of imaging spectrometer classification algorithms. However, standard reference data scenes typically are not sufficiently detailed to support assessment of spectral unmixing algorithms. Furthermore, commonly used reference data often lack validation reports that estimate error in the reference data itself,...
Spectral Unmixing is a challenging and absorbing problem. Unmixning allows us to break down a pixel's composition into its material components. Many avenues of spectral unmixing have been attempted with considerable success. One such avenue is to frame the spectral unmixing problem as an Estimation-Measurement problem and avail the use of the well-known Kalman Filter (KF) technique. Two such recent...
NorthStar: “Changing the way we see the world.” This paper introduces the NorthStar constellation of satellites as a new era in remote sensing that will respond to urgent terrestrial and spaceborne information needs. The NorthStar System of Systems includes Earth Observation (EO) and Space Situational Awareness (SSA) modules, with a key unifying theme for identifying and extracting meaningful and...
The finer spatial, spectral and radiometric resolutions of current and planned sensors are rendering increasingly-high data rates which, coupled with limited on-board storage, downlink bandwidth and receiving ground station availability, make high-throughput, high-performance data-reduction techniques essential in forthcoming missions. On this paper we describe an algorithm well suited to high-dimensional...
The synergistic analysis of light detection and ranging (LiDAR) and hyperspectral data is attracting a significant interest in recent years due to the complementary nature of these two sources of remote sensing data. In this paper, we propose a new spectral-spatial classification method able to jointly exploit these two kinds of data. Our work is based on three innovative components: 1) a superpixel...
Signature-based detectors for hyperspectral target detection rely on knowing the specific target signature in advance. However, target signatures are often difficult or impossible to obtain. Furthermore, common methods for obtaining target signatures, such as from laboratory measurements or manual selection from an image scene, usually do not capture the discriminative features of target class. In...
Classification of objects, materials or terrain in hyperspectral imagery requires the definition of an appropriate measure of spectral similarity, typically expressed in terms of spectral reflectance. For many objects, absolute reflectance varies due to bidirectional reflectance distribution function (BRDF) effects or uneven illumination. Here, an appropriate similarity measure is spectral angle;...
When performing point target detection in hyperspectral imagery, one often uses the spectral inverse covariance matrix to whiten the natural noise of the image. Since the cube is not necessarily stationary, we wish to understand when segmentation is worthwhile to provide different covariance matrices for different areas of the cube. Using simulations and several new analytical tools, we propose general...
In hyperspectral target detection, a hyperspectral image is usually collected from an airborne or satellite platform, and the goal is to identify all occurrences of a particular target material within that image. When the target of interest can have a single relatively stable reference spectrum, e.g., as with a chemical plume, then the detection algorithms are relatively straightforward. When the...
This paper will discuss recent improvements made to the Monte Carlo Scene (MCScene) code to enable limb-viewing scenarios and situations where the sun is below the horizon. MCScene is a high-fidelity model for full optical spectrum (UV through LWIR) hyperspectral image (HSI) simulation. MCScene generates HSI scenes for algorithm validation, utilizing a Direct Simulation Monte Carlo (DSMC) approach...
This paper introduces a methodology for predicting the year of plantation (YOP) from remote sensing data. The application has important implications in forestry management and inventorying. We exploit hyperspectral and LiDAR data in combination with state-of-the-art machine learning classifiers. In particular, we present a complete processing chain to extract spectral, textural and morphological features...
The future launch of EnMAP satellite in 2019 will enable the acquirement of hyperspectral data for obtaining more accurate relevant surface parameters on a global scale. This paper will discuss the application of EnMAP in extracting impervious surface area (ISA) from urban-rural gradient scenes. Considering the need for ISA extraction in urban studies, the choice of endmembers are made of five mixed...
Knowledge of calibration traceability is essential for ensuring the quality of data products relying on multiple sensors and especially true for imaging spectrometers. The current work discusses the expected impact that imaging spectrometers have in ensuring radiometric traceability for both multispectral and hyperspectral products. The Climate Absolute Radiance and Refractivity Observatory Pathfinder...
We propose LOCO-CV-GP, a method for cross-validating Gaussian process (GP) methods in a leave-one-crown-out (LOCO) manner, when the GP method is applied on hyperspectral data from tree crowns. The fact that spectra within a crown are correlated [1] needs to be taken into consideration when working with airborne HS tree spectra. The experiments are conducted on OSBS2014 dataset to cross-validate OGP,...
A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral version of the SLIC superpixel algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to obtain a final segmentation. The proposed method is applied to two...
Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step...
We formulate hyperspectral target detection in terms of a local context by modeling the relationship of individual pixels with the annuli of pixels that surround them. A prediction of the center pixel in terms of the annulus pixels provides an estimate of the target-free pixel value, and this estimate can be used as a baseline against which a measurement of that pixel is compared. When the measurement...
There are many types of natural gas fields including shale formations which are common especially in the St-Lawrence Valley (Canada). Since methane (CH4), the major component of shale gas, is odorless, colorless and highly flammable, in addition of being a greenhouse gas, methane emanations and/or leaks are important to consider for both safety and environmental reasons. On this regard, passive remote...
Within the framework of responsible mining, a SOLSA project (www.solsa-mining.eu) to develop an in-situ tool allowing a quick mineralogical identification of site drill cores has been recently launched. Its objective is to develop new or improved highly-efficient and cost-effective, sustainable exploration technologies. It combines and integrates non-destructive sensors: X-ray fluorescence, X-ray...
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