ABSTRACT
There is a well-known trade-off between the spectral and spatial resolutions of the resultant imagery in remote sensing because of technical constraints, and this limitation adversely impacts the exploitation of remotely sensed imagery for different real world applications, especially for those requiring both high spatial and spectral resolution imageries. With the advancement of remote sensing technologies, a variety of remotely sensed imagery from different sensors and platforms with different spatial, temporal, and spectral characteristics has become available. Therefore, it is advisable to blend multi-sensor observations together to facilitate further feature extraction and decision analysis. Such a blending process is referred to as data or image fusion. The objective of this chapter is to introduce the concepts and basic principles of image and data fusion as well as the existing practices of contemporary and classic data fusion technology hubs that fuse various data sources for advancing environmental monitoring systematically. This chapter will describe the following image and data fusion technology hubs although some cases are intertwined with each other:
Multispectral remote sensing-based fusion techniques,
Hyperspectral remote sensing-based fusion techniques, and
Microwave remote sensing-based fusion techniques.
Real world applications of different methods for fusing various types of data sources are also presented to demonstrate the capability and effectiveness of each fusion method.
