Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste sites have the potential to contain problematic trace element species (e.g., U, Pb, Cr). In our research, we aim to combine the mapping and mon...

Although it is well recognized that photointerpretation of the Landsat imagery can provide adequate information for mapping broad categories of land cover at a reconnaissance level, it is also true that the Landsat data in digital format (CCT's) together with numerical (computer-aided) analysis techniques can provide a great deal more information at a higher level of mapping detail with mapping...

We investigated the Classification of satellite images and multispectral remote sensing data .we focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area and green area. We carried out classification in three modules namely (a) Preprocess...

This paper describes applications of linear and nonlinear projection methods, in order to obtain a reduction of the number of spectral bands in LANDSAT multispectral images. We present Curvilinear Component Analysis (CCA, nonlinear method) and an optimisation of it based on the use of Principal Component Analysis (PCA, linear method). In order to evaluate the pertinence of the information kept ...

The authors demonstrate an improved differentiation of the most common tissue types in the human brain and surrounding structures by quantitative validation using multispectral analysis of magnetic resonance images. This is made possible by a combination of a special training technique and an increase in the number of magnetic resonance channel images with different pulse acquisition parameters...

The use of Unmanned Aerial Vehicles (UAVs) equipped with spectral cameras has increased in recent years, especially the agricultural sector, because it allows farmers and researchers to analyze state a crop, i.e., health, nutrients, growth, epidemics, among other parameters. In Colombia, coffee production sector faces several challenges, such as need increase productivity, yield, quality coffee...

Automated segmentation of tumors from a multispectral data set like that of the Magnetic Resonance Images (MRI) is challenging. Independent Component Analysis (ICA) and its variations for Blind Source Separation (BSS) have been employed in previous studies but have met with cumbersome obstacles due to its inherent limitations. Here we have approached the multispectral data set initially with fe...

Medical scans are today routinely acquired using multiple sequences or contrast settings, resulting in multispectral data. For the automatic analysis of this data, the evaluation of multispectral similarity is essential. So far, few concepts have been proposed to deal in a principled way with images containing multiple channels. Here, we present a new approach based on a well known statistical ...

Feature selection and dimensionality reduction are crucial research fields in pattern recognition. This work presents the application of a novel technique on dimensionality reduction to deal with multispectral images. A distance based on mutual information is used to construct a hierarchical clustering structure with the multispectral bands. Moreover, a criterion function is used to choose auto...