Customer Reviews

Computer Processing of Remotely-Sensed Images: An Introduction, 2nd Edition

5 star:		(3)
4 star:		(3)
3 star:		(0)
2 star:		(0)
1 star:		(0)

 

 

I checked the book out from my library to read for my thesis, but the book is so good that I want to add it to my own collection. I have used it so much, that I'll need my own copy! The CD that comes with the book is also very good. The programs run very quickly on today's fast PC's. It's a great tool.

I bought it just looking at the table of contents at ... website, and I got exactly what I was looking for !! A complete book on image processing/interpretation for remote-sensing imageries. There is even a concise introduction on remote-sensing principles. The book is concisely detailed and have clear how-to-do theoretical/mathematical explanations(often lacking in others) on every topic. And the best point, every section/method is referred to key publicatons for anyone interested in in-depth study of a topic !!!

The delivery was fast and ahead of time. The text was appropriate to my need.

I have to commend the author on not reissuing a minorly revised textbook just because this is one of the few in the field and he knows he can reap rewards by putting the used books out of commission. The following is the table of contents for the third edition, with greatly revised or new chapters marked with an asterisk:

CHAPTER 1: REMOTE SENSING: BASIC PRINCIPLES
1.1 Introduction
1.2 Electromagnetic radiation and its properties
1.2.1 Terminology
1.2.2 Nature of electromagnetic radiation
1.2.3 The electromagnetic spectrum
1.2.4 Sources of electromagnetic radiation
1.2.5 Interactions with the Earth's atmosphere
1.3 Interaction with Earth-surface materials
1.3.1 Introduction
1.3.2 Spectral reflectance of Earth surface materials
1.3.2.1 Vegetation
1.3.2.2 Geology
1.3.2.3 Water bodies
1.3.2.4 Soils
1.4 Summary

CHAPTER 2: REMOTE SENSING PLATFORMS AND SENSORS
2.1 Introduction
2.2 Characteristics of imaging remote sensing instruments
2.2.1 Spatial resolution
2.2.2 Spectral resolution
2.2.3 Radiometric resolution
2.3 Optical, near-infrared and thermal imaging sensors
2.3.1 Along-Track Scanning Radiometer (ATSR)
2.3.2 Advanced Very High Resolution Radiometer (AVHRR)
2.3.3 MODIS (MODerate Resolution Imaging Spectrometer)*
2.3.4 Ocean observing instruments*
2.3.5 IRS-1 LISS
2.3.6 Landsat Instruments
2.3.6.1 Landsat Multi-spectral Scanner (MSS)
2.3.6.2 Landsat Thematic Mapper (TM)
2.3.6.3 Enhanced Thematic Mapper Plus (ETM+)*
2.3.6.3 Landsat follow-on programme*
2.3.7 SPOT sensors
2.3.7.1 SPOT High Resolution Visible (HRV)
2.3.7.2 Vegetation (VGT)
2.3.7.3 SPOT follow-on program*
2.3.8 Advanced Spaceborne Thermal Emission and Reflection Radiometer*
2.3.9 High-resolution commercial and micro-satellite systems*
2.3.9.1 High-resolution commercial satellites - IKONOS*
2.3.9.2 High-resolution commercial satellites - QuickBird*
2.4 Microwave imaging sensors
2.4.1. ERS SAR
2.4.2 RADARSAT
2.5 Summary

CHAPTER 3: HARDWARE AND SOFTWARE ASPECTS OF DIGITAL IMAGE PROCESSING*
3.1 Introduction*
3.2 Properties of digital remote sensing data*
3.2.1 Digital data*
3.2.2 Data formats*
3.2.3 System processing*
3.3 MIPS software*
3.3.1 Installing MIPS*
3.3.2 Using MIPS*
3.3.3 Summary of MIPS functions*
3.4 Summary*

CHAPTER 4: PRE-PROCESSING OF REMOTELY SENSED DATA
4.1 Introduction
4.2 Cosmetic operations
4.2.1 Missing scan lines
4.2.2 De-striping methods
4.2.2.1 Linear method
4.2.2.2 Histogram matching
4.2.2.3 Other destriping methods
4.3 Geometric correction and registration
4.3.1 Orbital geometry model
4.3.2 Transformation based on ground control points
4.3.3 Resampling procedures
4.3.4 Image registration
4.3.5 Other geometric correction methods
4.4 Atmospheric correction
4.4.1 Background
4.4.2 Image-based methods
4.4.3 Radiative transfer models
4.4.4 Empirical line method
4.5 Illumination and view angle effects
4.6 Sensor calibration
4.7 Terrain effects
4.8 Summary

CHAPTER 5: IMAGE ENHANCEMENT TECHNIQUES
5.1 Introduction
5.2 Human visual system
5.3 Contrast enhancement
5.3.1 Linear contrast stretch
5.3.2 Histogram equalisation
5.3.3 Gaussian Stretch
5.4 Pseudocolour enhancement
5.4.1 Density slicing
5.4.2 Pseudocolour transform
5.5 Summary

CHAPTER 6: IMAGE TRANSFORMS
6.1 Introduction
6.2 Arithmetic operations
6.2.1 Image addition
6.2.2 Image subtraction
6.2.3 Image multiplication
6.2.4 Image division and vegetation ratios
6.3 Empirically based image transforms
6.3.1 Perpendicular Vegetation Index
6.3.2 Tasselled Cap (Kauth-Thomas) transformation
6.4 Principal Components Analysis
6.4.1 Standard Principal Components Analysis
6.4.2 Noise-adjusted Principal Components Analysis*
6.4.3 Decorrelation stretch
6.5 Hue, Saturation and Intensity (HSI) transform
6.6 The Discrete Fourier Transform
6.6.1 Introduction
6.6.2 Two-dimensional DFT
6.6.3 Applications
6.7 The Discrete Wavelet Transform*
6.7.1 Introduction*
6.7.1 The one-dimensional Discrete Wavelet Transform*
6.7.2 The two-dimensional Discrete Wavelet Transform*
6.8 Summary

CHAPTER 7: FILTERING TECHNIQUES
7.1 Introduction
7.2 Spatial domain low-pass (smoothing) filters
7.2.1 Moving average filter
7.2.2 Median filter
7.2.3 Adaptive filters
7.3 Spatial domain high-pass (sharpening) filters
7.3.1 Image subtraction method
7.3.2 Derivative-based methods
7.4 Spatial domain edge detectors
7.5 Frequency domain filters
7.6 Summary

CHAPTER 8: CLASSIFICATION
8.1 Introduction
8.2 Geometrical basis of classification
8.3 Unsupervised classification
8.3.1 The k-means algorithm
8.3.2 ISODATA
8.3.3 A modified k-means algorithm
8.4 Supervised classification
8.4.1 Training samples
8.4.2 Statistical classifiers
8.4.2.1 Parallelepiped classifier
8.4.2.2 Centroid (k-means) classifier
8.4.2.3 Maximum likelihood method
8.4.3 Neural classifiers
8.5 Fuzzy classification and linear spectral unmixing
8.5.1 The linear mixture model
8.5.2 Fuzzy classifiers
8.6 Other approaches to image classification
8.7 Incorporation of non-spectral features
8.7.1 Texture
8.7.2 Use of external data
8.8 Contextual information
8.9 Feature selection
8.10 Classification accuracy
8.11 Summary

CHAPTER 9: ADVANCED TOPICS*
9.1 Introduction*
9.2 SAR Interferometry*
9.2.1 Basic principles*
9.2.2 Interferometric processing*
9.2.3 Problems in SAR interferometry*
9.2.4 Applications of SAR interferometry*
9.3 Imaging spectrometry*
9.3.1 Introduction*
9.3.2 Processing imaging spectrometer data*
9.3.2.1 Derivative analysis*
9.3.2.2 Smoothing and denoising the reflectance spectrum*
9.3.2.2.1 Savitzky-Golay polynomial smoothing*
9.3.2.2.2 Denoising using the Discrete Wavelet Transform*
9.3.2.3 Determination of `red edge' characteristics of vegetation*
9.3.2.4 Continuum removal*
9.4 Lidar*
9.4.1 Introduction*
9.4.2 Lidar details*
9.4.3 Lidar applications*

There are sections of this book that are done in other books in a more thorough fashion that any student of this subject must have. To begin with, the subject matter on image processing algorithms in chapters five through seven is better handled in Digital Image Processing (3rd Edition). There is a MATLAB version of that book by the way - Digital Image Processing Using MATLAB(R). Chapter eight's material is better covered in both Remote Sensing Digital Image Analysis: An Introduction and in a general way in Machine Learning (Mcgraw-Hill International Edit) - an oldie but a goodie. As for chapter three - "Hardware and software aspects of digital processing" - do we really need another chapter to update us on why using a computer for this work is necessary and how the different pieces of software and hardware in a computer operate? Half of the chapter is devoted to the MIPS language - understanding it and installing it. I desire to do neither. I use Matlab for calculations and Java or Python for GUIs, and I imagine most professionals in this field do so too. Like Donald Knuth, author of the essential and elegant "Art of Computer Programming" books, this author has allowed a great book to become mired down in an obscure language rarely used outside of academia.

So what is really good about this book to merit four stars? Chapters 1,2, and 4 are well-written chapters on the basics, sensors, and pre-processing of remotely sensed data that is hard to find in other volumes. The author takes the time to explain this subject matter clearly and in detail. Then, there is chapter nine on advanced topics. The chapter is a work of art on topics I have found elsewhere, but poorly explained. These four chapters alone - 1,2,4, and 9 - are worth the price of the book, which is very affordable by graduate/senior undergraduate textbook standards. I highly recommend it. If only the author had used Matlab, this would have been a six star book.

Easy to understand. It is essential to a researcher. It would have been rated as an excellent book if it includes the Matlab programming and Erdas rather than any other software like MIPS.

Matlab based programs would have been more beneficial because many of them use matlab only.