Skip to Content
Hyperspectral Data Processing: Algorithm Design and Analysis
book

Hyperspectral Data Processing: Algorithm Design and Analysis

by Chein-I Chang
April 2013
Intermediate to advanced
1164 pages
39h 37m
English
Wiley-Interscience
Content preview from Hyperspectral Data Processing: Algorithm Design and Analysis

2.1 Introduction

A subsample target can appear in two different forms. One is a target that is smaller than the sample spatial resolution, in which case the target is embedded in a sample. An example is a vehicle with size of img that can be completely embedded in a single pixel with resolution of img. Another is a target that partially occupies a sample with certain amount of abundance fraction. That is, a target may or may not have its size greater than the sample resolution but it occupies more than one sample with partial abundance fractions. In either case, such a target is considered as a subsample target since it does not fully occupy an entire sample. It should be noted that as shown in Sections 30.2–30.3 in Chapter 30 the abundance fraction of a target contained in a sample can be used to calculate the partial size of the target occupying the sample.

A mixed sample is considered as a sample mixed with a number of target substances with appropriate abundance portions that account for the entire sample. A distinction between a subsample target and a mixed sample is that the latter must have the knowledge of all the target substances present in the sample compared to the former that only needs to know that the target of interest resides in the sample while discarding the information ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

AirBnbBlueOriginElectronic ArtsHomeDepotNasdaqRakutenTata Consultancy Services

QuotationMarkO’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.
Julian F.
Head of Cybersecurity
QuotationMarkI wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.
Addison B.
Field Engineer
QuotationMarkI’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.
Amir M.
Data Platform Tech Lead
QuotationMarkI'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Mark W.
Embedded Software Engineer

You might also like

Computer Vision Technology in the Food and Beverage Industries

Computer Vision Technology in the Food and Beverage Industries

D-W Sun
Deep Learning through Sparse and Low-Rank Modeling

Deep Learning through Sparse and Low-Rank Modeling

Zhangyang Wang, Yun Fu, Thomas S. Huang
Multimodal Scene Understanding

Multimodal Scene Understanding

Michael Ying Yang, Bodo Rosenhahn, Vittorio Murino

Publisher Resources

ISBN: 9781118269770Purchase book