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Band selection, by choosing a set of representative bands in hyperspectral images (HSI), is concerned to be an effective method to eliminate the “Hughes phenomenon”. In this paper, we present a global optimal clustering-based band selection (GOC) algorithm based on the hypothesis that all the bands in a cluster are continuous at their wavelengths. After the clustering result is obtained, we propose...
Kernel based feature extraction method overcomes the curse of dimensionality and captures the non-linearities present in the data. However, these methods are not scalable with large number of pixels found with hyperspectral images. Thus, a small subset of pixels are randomly selected to make the solution of kernel based methods tractable. In this paper, we propose scalable nonlinear component analysis...
Graph embedding, as a dimensionality reduction framework, has already drawn great attention in hyperspectral image analysis. Taking locality preserving projection (LPP) as example, LPP utilizes typical Euclidean distance in heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with...
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...
Hyperspectral unmixing frameworks are ultimately designed to understand and quantify the actual distribution of endmembers in a given scene. Assessing the percentage of each material is typically cumbersome, especially in images characterized by complex combinations of spectral signatures. In this work, we present a nonlinear programming scheme that aims at providing direct estimation of the endmembers...
Endmember extraction is a fundamental task in spectral unmixing of remotely sensed hyperspectral images. In this work, we develop a new robust algorithm for endmember extraction which is based on a nonnegative sparse autoencoder. The proposed approach is based on two main steps. First, it uses an automatic sampler approach with local outlier factor and affinity propagation to intelligently gather...
Spectral unmixing is to decompose the hyperspectral data into endmembers and abundances. It has been known to be a challenging and ill-posed task due to the corruption of noise as well as complex environmental conditions. In this paper, we propose a part-based denoising autoencoder with unique structure that solves the unmixing challenges. The effective l21 norm and denoising constraints are applied...
Several classes of endmember (EM) extraction algorithms based on the pure pixel assumption exist. Most of these algorithms employ some geometrical interpretation of the spectral mixing process, and use orthogonal projections, random projections, or some combination of them. Random projection based algorithms, such as pixel purity index, often find clusters of EM candidates which show high correlation,...
In this paper, we present a fast blind multitemporal hyperspectral unmixing algorithm, using an l1 penalty to promote sparse abundances. The method is able to account for different acquisition conditions of multitemporal images, by allowing the spectral signatures in the different temporal images to vary. The new algorithm is tested on simulated data and applied on real hyperspectral data.
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...
Sparse unmixing of hyperspectral data is an important technique which aims at estimating the fractional abundances of endmembers (pure spectral components). It is well known that enforcing sparseness becomes a necessary process in sparse unmixing methods. To better exploit the sparsity in hyperspectral imagery, a double reweighted sparse unmixing algorithm has been proposed. However, it focusses on...
In this paper, hyperspectral data is modeled as a combination of a sparse component, a low rank component and noise. The low rank component is a product of the endmembers and the abundances in an image, and the sparse component is composed of outliers and structured noise. Outliers and structured noise in this context are, e.g. band specific noise, vertical or horizontal artifacts or saturated pixels...
EnMAP (Environmental Mapping and Analysis Program) is a German spaceborne imaging spectrometer Earth observing mission planned for launch in 2019. This paper reflects the status of the mission with an focus to changes of the Ground Segment based on a major review conducted in 2016 and the EnMAP Data Exploitation and Application Development Program and recent activities.
Hyperspectral Imager Suite (HISUI) is a future spaceborne hyperspectral Earth imaging system being developed by Japanese Ministry of Economy, Trade, and Industry (METI). HISUI will be launched and deployed on International Space Station (ISS) for three year operation from 2019. In FY2016, manufacturing and testing of HISUI Flight Model and design of HISUI Exposed Payload System were conducted. HISUI...
The Canadian Space Agency (CSA) in collaboration with the Naval Research Laboratory (NRL) and NASA are considering a coastal and inland water color hyperspectral imager as a complement to the NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. This hyperspectral imager with 100 m spatial sampling would be specifically designed to sample the coastal oceans, estuaries and lakes, and will...
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...
This paper introduces a two-step hyper- and multi-spectral image classification approach. The first step relies on the use of a genetic programming (GP) framework to both select and combine appropriate bands. The second step is concerned with the image classification itself. We present two strategies for multi-class classification problems based on the combination of GP-based indices defined in binary...
Unsupervised manifold learning has become accepted as an important tool for reducing dimensionality of a data set by finding its meaningful low dimensional representation lying on an unknown nonlinear subspace. Most manifold learning methods only embed an existing data set, but do not provide an explicit mapping function for novel out-of-sample data, thereby potentially resulting in an ineffective...
Multiple support vector machines (SVMs) with random subspaces [1]-[5] have been performing excellently for hyperspectral image classification to reduce the correlation between features and avoid the Hughes phenomena. In most random subspace methods, features were randomly selected without replacement from the original feature set according to uniform distribution [6]. However, in general, SVM with...
Anomaly detection (AD) is designed to find targets that are spectrally distinct from their surrounding neighborhood. Unfortunately, commonly used anomaly detectors generally do not take into account its surrounding spatial information. This paper derives an iterative version of anomaly detection, iterative anomaly detection (IAD) to address this issue. Its idea is to use a Gaussian filter to capture...
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