This paper presents genetic algorithm based band selection and classification on hyperspectral image data set. Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. In this paper, first filtering based on 2-D Emp...

We describe a new pushbroom hyperspectral imaging device that has no macro moving part. The main components of the proposed hyperspectral imager are a digital micromirror device (DMD), a CMOS image sensor with no filter as the spectral sensor, a CMOS color (RGB) image sensor as the auxiliary image sensor, and a diffraction grating. Using the image sensor pair, the device can simultaneously capt...

Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important in agriculture, environmental monitoring, urban planning, mining, and defense...

Accurate and in vivo characterization of structural, functional, and molecular characteristics of biological tissue will facilitate quantitative diagnosis, therapeutic guidance, and outcome assessment in many clinical applications, such as wound healing, cancer surgery, and organ transplantation. We introduced and tested a multiview hyperspectral imaging technique for noninvasive topographic im...

In this paper, we discuss about hyperspectral image processing where it plays an important role in remote sensing, hyperspectral verses multispectral image processing and image classifications. Where these classifications includes image sensors, image preprocessing, object detection, object segmentation, feature extraction and object classification. Mainly there are two types of classifications...

BACKGROUND Segmentation of hyperspectral medical images is one of many image segmentation methods which require profiling. This profiling involves either the adjustment of existing, known image segmentation methods or a proposal of new dedicated methods of hyperspectral image segmentation. Taking into consideration the size of analysed data, the time of analysis is of major importance. Therefor...

Since hyperspectral imagery (HSI) (or remotely sensed data) provides more information (or additional bands) than traditional gray level and color images, it can be used to improve the performance of image classification applications. A hyperspectral image presents spectral features (also called spectral signature) of regions in the image as well as spatial features. Feature reduction, selection...

This work deals with hyperspectral image analysis in the absence of ground-truth. The method adopts a projection pursuit (PP) procedure with entropy index to reduce the dimensionality followed by Markov Random Field (MRF) model based segmentation. Ordinal optimization approach to PP determines a set of “ good enough projections” with high probability the best among which is chosen with the help...

In the past several decades, hyperspectral imaging has drawn a lot of attention in the field of remote sensing. Yet, due to low spatial resolutions of hyperspectral imagers, often the response from more than one surface material can be found in some hyperspectral pixels. These pixels are called mixed pixels. Mixed pixels bring challenges to traditional pixel-level applications, such as identifi...

A hyperspectral remotely sensed image may be modeled as a linear mixture of the spectral responses of unknown spectral endmembers. Using the a-priori information that the unknown spectral abundance images should be spatially homogenous, a simple associative neural network may be trained using Hebbian learning to extract spectral endmembers and corresponding abundance images from a hyperspectral...