In order to determine mineralogy of rock and soil samples, reflectance and emittance spectroscopy in the near-infra red and short-wave infra-red is used extensively and found to be inexpensive. Hyper spectral remote sensing satellite data are found to be prospective to deliver in depth physico-chemistry like mineralogy, chemistry, morphology of the earth’s surface. Therefore hyper spectral data...

The recent surge in remotely sensed imagery with multi to hyper spectral cubes has made it very difficult to detect features because of the sheer volume of data. In a sense it is locating the needle in the haystack which in urban areas is extremely difficult unless we have specific knowledge of the anomaly spectrum. Adding noise and atmospheric masking makes it even more complex a problem. In t...

The purpose of the AMDF Cluster is to investigate the benefits that data fusion, and related techniques, may bring to future military Intelligence Surveillance Target Acquisition and Reconnaissance (ISTAR) systems. In the course of this work it is intended to showcase the practical application of some of the best multi-dimensional fusion research in the United Kingdom. This paper highlights wor...

Dear Editor, This letter presents an open-set classification method of remote sensing images (RSIs) based on geometric-spectral reconstruction learning. More specifically, in order to improve the ability RSI model adapt environment, geometric and spectral feature fusion is proposed. proposes realize features with hyper-spectral light detection ranging (LiDAR) data for first time. In a variety s...

This paper focuses on the application of Extreme Learning Machines (ELM) to the classification of remote sensing hyperspectral data. The specific aim of the work is to obtain accurate thematic maps of soybean crops, which have proven to be difficult to identify by automated procedures. The classification process carried out is as follows: First, spectral data is transformed into a hyper-spheric...

Graphics Processing Units (GPU) are becoming a widespread tool for general-purpose scientific computing, and are attracting interest for future on board satellite image processing payloads due to their ability to perform massively parallel computations. This paper describes the GPU implementation of an algorithm for on board loss hyper spectral image compression and proposes an architecture tha...

Images of representative clayey bare fields were chosen from a high spatial resolution hyperspectral dataset that was acquired with the prototypal airborne Hyper SIM-GA sensor from Selex Galileo simultaneously with ground soil spectral signatures and the collection of samples. After both the pre-processing and calibration of the SIM-GA data, the mapping procedure was developed using the 2-2.45 ...

This paper reports an investigation of features relevant for classifying two speaking styles, namely, conversational speaking style and clear (e.g. hyper-articulated) speaking style. Spectral and prosodic features were automatically extracted from speech and classified using decision tree classifiers and multilayer perceptrons to achieve accuracies of about 71% and 77% respectively. More intere...

This invited paper briefly describes our progress in developing a resilient multi-spectral image analysis capability for remote sensing applications. This capability is intended to allow image streams from a collection of distributed sensors to be disseminated and interpreted by a group of analysts, while under information warfare attack. There are five component technologies that we are develo...

Spectral analysis has a very high sensitivity and accuracy, widely used in qualitative and quantitative analysis of textile fibers. This paper described the mechanism of spectroscopy in qualitative identification of textile fibers. Then it gave a systematic review and summary on the research of qualitative or quantitative analysis of textile fibers. It discussed the mechanism of spectroscopy in...