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

17.4 Synthetic Image Experiments

Two of six synthetic image-based scenarios in Chapter 4, TI2 in Section 4.3.2.2 and TE2 in Section 4.3.3.2, are particularly selected for experiments and re-described as follows.

Target Implantation 2

The target implantation 2 (TI2) inserts a number of panel pixels into the image by replacing their corresponding BKG pixels. So, the resulting synthetic image has clean panel pixels with perfect knowledge implanted in a noisy BKG corrupted an additive Gaussian noise with a certain level of SNR. TI2 is primarily designed to simulate scenarios with pure pixels implanted as pure signatures to represent true endmembers to evaluate the quantitative performance of SLSMA.

Target Embededness 2

As opposed to TI, the second type of target insertion is target embededness 2 (TE2) that is the same as the TI2 described above except the way the panel pixels are inserted. The BKG pixels were not removed to accommodate the inserted panel pixels as they are done in TI2, but were rather superimposed over the inserted panel pixels. So, in this case, the resulting synthetic image has clean panel pixels embedded in a noisy BKG. The TE2 is particularly designed to simulate scenarios where there are no pure pixels present in the data. As a result, no real true endmembers can be used for LSMA. So, TE2 is more realistic than TI2. Instead, the VSs must be found from TE2. Nevertheless, the complete knowledge of inserted panels and BKG signature is still available for quantitative ...

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