Hyperspectral Data Processing: Algorithm Design and Analysis

2.5 Conclusions

Subsample detection and mixed sample classification play central roles in hyperspectral image analysis. However, it seems that there is a lack of detailed treatment on decision issues of these two topics. Generally speaking, a subsample is a target sample of interest embedded in a sample with an unknown proportion while the remaining proportion of its embedded sample is considered as the background. On the other hand, a mixed sample comprises a set of known target signatures mixed linearly or nonlinearly in a sample. In light of this interpretation a major difference between a subsample and a mixed sample is that the background of a sample in which a subsample is embedded is unknown, while the background of a mixed sample is completely specified by other known target signatures. As a result, there is no background issue in a mixed sample and a mixed sample is generally performed by classification. By contrast, the background of a subsample is generally unknown and unspecified. Therefore, a subsample is usually performed by detection rather than by classification and the effectiveness of the subtarget detection is heavily determined by background suppression. Because of that this chapter is devoted to detection and classification via two types of decision-making processes, hard decisions and soft decisions, to address the issues of subsamples and mixed samples. As for detection, two approaches are reviewed. One is the likelihood ratio test using a threshold in terms ...