Automatic Preprocessing and Classification System for High Resolution Ultra and Hyperspectral Images

Introduction

Spectroradiometric imaging is based on the fact that all materials display a specific spectral signature when light impinges on them. That is, they reflect, absorb, and emit electromagnetic waves in a particular pattern depending on the wavelength, leading to a spectrum for the material that can be used to classify it. An imaging spectroradiometer captures images where the spectral information of every pixel is collected for a set of contiguous discrete spectral bands. It is customary to classify these instruments as ultraspectral, hyperspectral, multispectral, and panchromatic imagers. Typically, ultraspectral data sets are made up of thousands of spectral bands, while hyperspectral sets have hundreds, multispectral tens, and panchromatic sets just a few. Thus, each ultraspectral or hyperspectral image contains large amounts of data which can be viewed as a cube with two spatial and one spectral dimension. Regardless of the application of the images, the analysis methods employed must deal with large quantities of data efficiently [1]. Originally, imaging spectroradiometers were used as airborne or spaceborne remote sensing instruments. During the last decade, spectral imaging has become a common advanced tool for remote sensing of the surface of Earth. It is being used to collect crucial data about many activities of economic or scientific relevance [2] as, for instance, mineral exploitation [3], agriculture and forestry use [4], environmental pollution [5], ecosystem studies [6], assessment of natural resources [7], water resource management [8], monitoring of coastal zones [9] and many others. For all these remote sensing applications the hyperspectral images obtained usually contain mixtures of spectra in every pixel due to the poor spatial resolution of the images when taken a large distance from the target. Thus a single pixel could typically correspond to one hundred or more square meters of land. As a result, most analysis methods developed had as their main objective to provide the segmentation of the images in terms of the ratio of endmembers present in every pixel to improve the spatial discrimination of these systems when analyzing different types of land covers [10].