Species classification is an important task when it comes to the study of specific areas without disrupting the ecosystem. In this paper we chose Indian Pines as the test site to classify it into 16 classes of tree species and compare them with ground truth to obtain the accuracy, using various algorithms with machine learning process. The algorithms used are LDA and SVM. Spectral features of t...

Initializing an unmixing matrix is an important problem in source separation since an objective function to be optimized is typically non-convex. In this paper, we consider the problem of two-source signal separation from a two-microphone array located on a mobile device, where a point source such as a speech signal is placed in front of the array, while no information is available about anothe...

Fraction of vegetation cover (FVC) is often estimated by unmixing a linear mixture model (LMM). In the LMM-based algorithm, differences can be seen in assumptions and constraints imposed to the model such as spectral vegetation index, inducing variations in algorithm. As a result, robustness against noises in reflectance spectrum is somewhat different among those algorithms, depending on a targ...

The ability to quantify the fluorescence signals from multiply labeled biological samples is highly desirable in the life sciences but often difficult, because of spectral overlap between fluorescent species and the presence of autofluorescence. Several so called unmixing algorithms have been developed to address this problem. Here, we present a novel algorithm that combines measurements of lif...

Introduction: A TES spectral study of low albedo (LA) deposits within the Amazonis Planitia of Mars revealed that the derived bulk composition of these deposits contain very low silica contents (Fig. 1), classifying some of the deposits as foidites [1]. A foidite is an igneous rock in which feldspathoid minerals constitute ≥60% of the light-colored mineralogy; it is the inclusion of feldspathoi...

Hyperspectral imaging, due to providing high spectral resolution images, is one of the most important tools in the remote sensing field. Because of technological restrictions hyperspectral sensors has a limited spatial resolution. On the other hand panchromatic image has a better spatial resolution. Combining this information together can provide a better understanding of the target scene. Spec...

It is common in the fields of rock and environmental magnetism to unmix magnetic mineral components using statistical methods that decompose various types of magnetization curves (e.g., acquisition, demagnetization, or backfield). A number of programs have been developed over the past decade that are frequently used by the rock magnetic community, however many of these programs are either outda...

Tensor-based methods have recently emerged as a more natural and effective formulation to address many problems in hyperspectral imaging. In hyperspectral unmixing (HU), low-rank constraints on the abundance maps have been shown to act as a regularization which adequately accounts for the multidimensional structure of the underlying signal. However, imposing a strict low-rank constraint for the...

In hyperspectral images, endmembers characterizing one class of ground object may vary due to illumination, weathering, slight variations the materials. This phenomenon is called intra-class endmember variability which important factors affecting performance unmixing. However, often ignored in unmixing, causes a decrease accuracy How deal with focus. To address this problem, we propose novel un...

In the North of Tunisia, Hammam Zriba region represents an important mine activity zone, it is among the most important in the Maghreb . The mine tailings cause the environment degradation, so it had polluted the atmosphere, the soils, the vegetation, the water quality and the ecosystem of the Hammam ouady (Dry River) which is discharged directly in the Miliane River. The Miliane River represen...