Image Processing Reference

In-Depth Information

Chapter 4

Band Selection Through Redundancy

Elimination

4.1 Introduction

Pixel-based image fusion techniques compute the fusion weights for each pixel

in every band. Thus, every band in the hyperspectral data is subjected to the

process of computing weights through some kind of saliency measure which is a

time-consuming operation. Let us divide the process of fusion into two steps- first,

computation of fusion weights through saliency measurement, and second, a linear

combination of the bands using these weights. The later step is quite trivial, and

requires a very little computation. The first step, however, is critical to the perfor-

mance of the algorithm, and can be computationally demanding as per the chosen

algorithm. While some attempts towards a quick visualization of the image contents

in the form of an RGB image have been investigated, some of these methods typically

include selection of three image bands satisfying certain criterion [49, 79]. However,

these approaches select only three bands for the display, and they do not involve any

kind of image merging or fusion at any level.

For the data (or pixel) level image fusion, most available techniques evaluate the

importance of a pixel within its spatial neighborhood, and then assign appropriate

weights to the pixels while fusing them over various image bands. For example, in

the previous chapter, the residual image after subtraction of the image band from

its bilateral filtered version provides the necessary fusion weights. Since this step

consumes the major amount of computation, usually on a per pixel basis, the time

taken for fusion is directly proportional to the number of image bands. An observer

has to wait until the completion of calculation of weights for the entire set of image

bands, followed by the successive weighted addition to get the final result of fusion.

Therefore, the fusion techniques tend to be slower due to the large number of image

bands. The process of calculation of weights can be computationally quite demand-

ing and time-consuming when the fusion techniques and /or the saliency detection

techniques are iterative in nature. This often limits us from exploring some sophis-

for hyperspectral image fusion.