Mathematical Principles of Remote Sensing: Making Inferences from Noisy Data [Hardcover]

Andrew S Milman (Author)

Book Description

ISBN-10: 1575041359 | ISBN-13: 978-1575041353 | Publication Date: March 1, 2000 | Edition: 1

This book is an informative reference, or working textbook, on the mathematics, and general physical and chemical processes behind remote sensor measurements.

Product Details

• Hardcover: 406 pages
• Publisher: CRC Press; 1 edition (March 1, 2000)
• Language: English
• ISBN-10: 1575041359
• ISBN-13: 978-1575041353
• Product Dimensions: 10.2 x 7 x 1.2 inches
• Shipping Weight: 2.5 pounds
• Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #3,952,071 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

4.0 out of 5 stars grt book, August 22, 2005

By 

Aarthy Sabesan (Knoxville, TN) - See all my reviews

This review is from: Mathematical Principles of Remote Sensing: Making Inferences from Noisy Data (Hardcover)

A great book to understand the whole scene creation process. if you are interested in venturing into the mathematics and physics of remote sensing, this is your book.

4.0 out of 5 stars Remote Sensing or Image Reconstruction, August 28, 2002

By 

D E Bodette (Albuquerque, NM United States) - See all my reviews

Amazon Verified Purchase

This review is from: Mathematical Principles of Remote Sensing: Making Inferences from Noisy Data (Hardcover)

If you are looking for a book on transforming satellite data into pretty pictures or land-use plots there are much better texts. Try those by Lillesand and Kieffer or Schowengerdt. However, if you are interested in the general area of inverse problems, especially unfolding the complex physical processes that produced the satellite imagery to extract information then this is a great book for you. The early chapters deal in general terms with the image formation process and sources of noise. These chapters are unsatisfying. The real gem of this book is Chapter 7 in which the author derives a robust, direct solution for linear matrix problems. Chapter 10 Integral Equations, Chapter 11 Iteration, and Chapter 12 Resolution and Noise are also very enlightening. The solutions and techniques covered in this text are equally applicable to other inverse problems such as image restoration or computerized tomography (CT).

The downside to this book is that it has quite a few errors.