hyperion hyperspectral data contains a very rich source of information from the earth surface that collects 242 narrow contiguous spectral bands. achieving this source of rich information is subject to the performance of suitable image processing methods on raw satellite data. satellite image processing methods can be classified into two categories of statistical-based and spectral-based. in th...

anomaly detection (ad) has recently become an important application of hyperspectral images analysis. the goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. one way to improve the performance and runtime of these algorithms is to use dimensionality reduction (dr) techniques. this paper evaluates the effect of thr...

hyperspectral sensors provide a large number of spectral bands. this massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. we propose to use overlap-based feature weigh...

Hyperspectral sensors provide a large number of spectral bands. This massive and complex data structure of hyperspectral images presents a challenge to traditional data processing techniques. Therefore, reducing the dimensionality of hyperspectral images without losing important information is a very important issue for the remote sensing community. We propose to use overlap-based feature weigh...

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...

A hyperspectral image is a large dataset in which each pixel corresponds to a spectrum, thus providing high-quality detail of a sample surface. Hyperspectral images are thus characterised by dual information, spectral and spatial, which allows for the acquisition of both qualitative and quantitative information from a sample. A hyperspectral image, commonly known as a “hypercube”, comprises two...

Image segmentation is a fundamental approach in the field of image processing and based on user’s application .This paper propose an original and simple segmentation strategy based on the EM approach that resolves many informatics problems about hyperspectral images which are observed by airborne sensors. In a first step, to simplify the input color textured image into a color image without tex...

The need for fast hyperspectral data processing methods is discussed. Discussion includes the necessity of faster processing techniques in order to realize emerging markets for hyperspectral data. Several standard hyperspectral image processing methods are presented, including maximum likelihood classification, principal components analysis, and canonical analysis. Modifications of those method...