A variety of multi-spectral imaging methods are discussed for acquiring spectral information from a scene. We first review conventional multispectral imaging approach. The conventional imaging systems are mostly constructed by multi-band imaging devices with different filtration mechanism at the sensor side under passive illumination. We show some imaging devices, estimation algorithms, and app...

Abstract Nowadays, laser communication has received great achievements owing to its advantages, e.g., large channel capacity, easy miniaturization of system and strong anti-interference ability in different conditions. Pulse position modulation (PPM), as an important technology that transmit signal with lower power, is attracting much attention. However, the performance scheme highly depends on...

Spectral analysis considers the problem of determining (the art of recovering) the spectral content (i.e., the distribution of power over frequency) of a stationary time series from a finite set of measurements, by means of either nonparametric or parametric techniques. This paper introduces the spectral analysis problem, motivates the definition of power spectral density functions, and reviews...

The availability of sufficient high quality information on the minefield scenery is an important prerequisite for the safety and the efficiency of all demining actions. Within the EU project ARC (Airborne Minefield Area Reduction) a system has been developed for airborne minefield survey for use in General Mine Action Assessment, Technical Survey (area reduction and as far a possible mine field...

A new, high-order, conservative, and efficient method for conservation laws on unstructured grids has been developed. It combines the best features of structured and unstructured grid methods to attain computational efficiency and geometric flexibility; it utilizes the concept of discontinuous and high-order local representations to achieve conservation and high accuracy; and it is based on the...

Spectral clustering enjoys its success in both data clustering and semisupervised learning. But, most spectral clustering algorithms cannot handle multi-class clustering problems directly. Additional strategies are needed to extend spectral clustering algorithms to multi-class clustering problems. Furthermore, most spectral clustering algorithms employ hard cluster membership, which is likely t...

Large data sets delivered by imaging spectrometers are interesting in many ways in the Planetary Sciences. Due to the size of the data, which often prohibits conventional exploratory data analysis, unsupervised analysis methods could be a way of gathering interesting information contained in the data. In this work, we investigate some of the opportunities and limitations of unsupervised analysi...

Dimensionality reduction is a major task in remote sensing images. Feature selection is applied for performing dimensionality reduction. It selects the spectral features(i.e. Bands) and find a feature subset that preserves the semantics of the hyperspectral image. Based on particle swarm optimization (PSO), this paper proposes multi-objective functions for selecting the spectral feature subsets...