1. Intro of digital image processing
Lecture 5a

2. Remote Sensing Raster (Matrix) Data Format
Digital number of column 5, row 4 at band 2 is expressed as BV5,4,2 = 105.

3. Image file formats BSQ (Band Sequential Format):
each line of the data followed immediately by the next line in the same spectral band. This format is optimal for spatial (X, Y) access of any part of a single spectral band. Good for multispectral images
BIP (Band Interleaved by Pixel Format):
the first pixel for all bands in sequential order, followed by the second pixel for all bands, followed by the third pixel for all bands, etc., interleaved up to the number of pixels. This format provides optimum performance for spectral (Z) access of the image data. Good for hyperspectral images
BIL (Band Interleaved by Line Format):
the first line of the first band followed by the first line of the second band, followed by the first line of the third band, interleaved up to the number of bands. Subsequent lines for each band are interleaved in similar fashion. This format provides a compromise in performance between spatial and spectral processing and is the recommended file format for most ENVI processing tasks. Good for images with bands

4. Matrix notation for band 220 50 90 76 66 55 45
120 80 60 70
150
100 93 97
101
105
120
150
100
103
176
166
155 85 80 70 77
135 90
133
210
250
190
245
156
166
155
415
220
180
160
170
200
123
222
215
Matrix notation for band 2
1,1,2
2,1,2
3,1,2
4,1,2
5,1,2
1,2,2
2,2,2
3,2,2
4,2,2
5,2,2
1,3,2
2,3,2
3,3,2
4,3,2
5,3,2
1,4,2
2,4,2
3,4,2
4,4,2
5,4,2 10 15 17 20 21 16 18 23 22 24 25 20 50 90 76 66 55 45
120 80 60 70
150
100 93 97
101
105
120
150
100
103
176
166
155 85 80 70 77
135 90
133
210
250
190
245
156
166
155
415
220
180
160
170
200
123
222
215
BIL 10 15 17 20 21 50 90
120
150
100
103
210
250
190
245 15 16 18 21 23 76 66 55 45
120
176
166
155 85
150
156
415
220 17 18 20 22 80 60 70
150 85 77
135
180
160
170
200 18 20 22 24 25
100 93 97
101
105
103 90 70
120
133
200
123
222
215
BSQ 10 20
120
210 15 76
176
156 16 17 80 85
180 18
100
103
200 50
150
250 17 66
166 18 55 80
180 20 60 93 90 22 97
100
250 20 90
120
155 21 45 85 70
160 22 77
123 24
101
190 21 90
103
245
415 23
120
150
220
170 22
135
200
222 25
105
133
215
BIP

5. Band sequential (BSQ) format stores information for the image one band at a time. In other words, data for all pixels for band 1 is stored first, then data for all pixels for band 2, and so on.
Value=image(c, r, b)
Band interleaved by pixel (BIP) data is similar to BIL data, except that the data for each pixel is written band by band. For example, with the same three-band image, the data for bands 1, 2 and 3 are written for the first pixel in column 1; the data for bands 1, 2 and 3 are written for the first pixel in column 2; and so on.
Value=image(b, c, r)
Band interleaved by line (BIL) data stores pixel information band by band for each line, or row, of the image. For example, given a three-band image, all three bands of data are written for row 1, all three bands of data are written for row 2, and so on, until the total number of rows in the image is reached.
Value=image(c, b, r)

6. What is image processing
Is enhancing an image or extracting information or features from an image
Computerized routines for information extraction (eg, pattern recognition, classification) from remotely sensed images to obtain categories of information about specific features.
Many more

7. Image Processing Includes
Image quality and statistical evaluation
Radiometric correction
Geometric correction
Image enhancement and sharpening
Image classification
Pixel based
Object-oriented based
Accuracy assessment of classification
Post-classification and GIS
Change detection
GEO5083: Remote Sensing Image Processing and Analysis, spring 2012

8. 1
Image Quality
Many remote sensing datasets contain high-quality, accurate data. Unfortunately, sometimes error (or noise) is introduced into the remote sensor data by:
the environment (e.g., atmospheric scattering, cloud),
random or systematic malfunction of the remote sensing system (e.g., an uncalibrated detector creates striping), or
improper pre-processing of the remote sensor data prior to actual data analysis (e.g., inaccurate analog-to-digital conversion).

9. 154
155
Cloud
155
160
162
MODIS
True
143
163
164

10. Clouds in ETM+

11. Striping Noise and Removal
CPCA
Combined Principle
Component Analysis
Xie et al. 2004

12. Speckle Noise and Removal
Blurred objects
and boundary
G-MAP
Gamma Maximum
A Posteriori Filter

13. Univariate descriptive image statistics
The mode is the value that occurs most frequently in a distribution and is usually the highest point on the curve (histogram). It is common, however, to encounter more than one mode in a remote sensing dataset.
The median is the value midway in the frequency distribution. One-half of the area below the distribution curve is to the right of the median, and one-half is to the left
The mean is the arithmetic average and is defined as the sum of all brightness value observations divided by the number of observations.

14. Cont’ Min Max Variance Standard deviation
Coefficient of variation (CV)
Skewness
Kurtosis
Moment

17. Multivariate Image Statistics
Remote sensing research is often concerned with the measurement of how much radiant flux is reflected or emitted from an object in more than one band. It is useful to compute multivariate statistical measures such as covariance and correlation among the several bands to determine how the measurements covary. Variance–covariance and correlation matrices are used in remote sensing principal components analysis (PCA), feature selection, classification and accuracy assessment.

18. Covariance
The different remote-sensing-derived spectral measurements for each pixel often change together in some predictable fashion. If there is no relationship between the brightness value in one band and that of another for a given pixel, the values are mutually independent; that is, an increase or decrease in one band’s brightness value is not accompanied by a predictable change in another band’s brightness value. Because spectral measurements of individual pixels may not be independent, some measure of their mutual interaction is needed. This measure, called the covariance, is the joint variation of two variables about their common mean.

19. Correlation
To estimate the degree of interrelation between variables in a manner not influenced by measurement units, the correlation coefficient, is commonly used. The correlation between two bands of remotely sensed data, rkl, is the ratio of their covariance (covkl) to the product of their standard deviations (sksl); thus:
If we square the correlation coefficient (rkl), we obtain the sample coefficient of determination (r2), which expresses the proportion of the total variation in the values of “band l” that can be accounted for or explained by a linear relationship with the values of the random variable “band k.” Thus a correlation coefficient (rkl) of 0.70 results in an r2 value of 0.49, meaning that 49% of the total variation of the values of “band l” in the sample is accounted for by a linear relationship with values of “band k”.

20. example Band 1 (Band 1 x Band 2) Band 2 130 7,410 57 165 5,775 35 100
Pixel
Band 1 (green)
Band 2 (red)
Band 3 (ni)
Band 4 (ni)
(1,1)
130 57
180
205
(1,2)
165 35
215
255
(1,3)
100 25
135
195
(1,4) 50
200
220
(1,5)
145 65
235
Band 1
(Band 1 x Band 2)
Band 2
130
7,410 57
165
5,775 35
100
2,500 25
135
6,750 50
145
9,425 65
675
31,860
232

21. Univariate statistics Band 1 Band 2 Band 3 Band 4 562.25 - 135 264.80
Mean (mk)
135
46.40
187
222
Variance (vark)
562.50
264.80
1007
570
(sk)
23.71
16.27
31.4
23.87
(mink)
100 25
195
(maxk)
165 65
215
255
Range (BVr) 40 80 60
Univariate statistics
Band 1
Band 2
Band 3
Band 4
562.25 -
135
264.80
718.75
275.25
537.50 64
663.75
570
Band 1
Band 2
Band 3
Band 4 -
0.35
0.95
0.53
0.94
0.16
0.87
covariance
Covariance
Correlation coefficient

22. Types of radiometric correction2
Types of radiometric correction
Detector error or sensor error (internal error)
Atmospheric error (external error)
Topographic error (external error)

23. Atmospheric correction
There are several ways to atmospherically correct remotely sensed data. Some are relatively straightforward while others are complex, being founded on physical principles and requiring a significant amount of information to function properly. This discussion will focus on two major types of atmospheric correction:
Absolute atmospheric correction, and
Relative atmospheric correction.
60 miles or
100km
Scattering, Absorption
Refraction, Reflection

24. Absolute atmospheric correction
Solar radiation is largely unaffected as it travels through the vacuum of space. When it interacts with the Earth’s atmosphere, however, it is selectively scattered and absorbed. The sum of these two forms of energy loss is called atmospheric attenuation. Atmospheric attenuation may 1) make it difficult to relate hand-held in situ spectroradiometer measurements with remote measurements, 2) make it difficult to extend spectral signatures through space and time, and (3) have an impact on classification accuracy within a scene if atmospheric attenuation varies significantly throughout the image.
The general goal of absolute radiometric correction is to turn the digital brightness values (or DN) recorded by a remote sensing system into scaled surface reflectance values. These values can then be compared or used in conjunction with scaled surface reflectance values obtained anywhere else on the planet.

25. a) Image containing substantial haze prior to atmospheric correction
a) Image containing substantial haze prior to atmospheric correction. b) Image after atmospheric correction using ATCOR (Courtesy Leica Geosystems and DLR, the German Aerospace Centre).

26. relative radiometric correction
When required data is not available for absolute radiometric correction, we can do relative radiometric correction
Relative radiometric correction may be used to
Single-image normalization using histogram adjustment
Multiple-data image normalization using regression

27. Single-image normalization using histogram adjustment
The method is based on the fact that infrared data (>0.7 m) is free of atmospheric scattering effects, whereas the visible region ( m) is strongly influenced by them.
Use Dark Subtract to apply atmospheric scattering corrections to the image data. The digital number to subtract from each band can be either the band minimum, an average based upon a user defined region of interest, or a specific value

28. Dark Subtract using band minimum

29. Topographic correction
Topographic slope and aspect also introduce radiometric distortion (for example, areas in shadow)
The goal of a slope-aspect correction is to remove topographically induced illumination variation so that two objects having the same reflectance properties show the same brightness value (or DN) in the image despite their different orientation to the Sun’s position
Based on DEM, sun-elevation

30. Conceptions of geometric correction3
Geocoding: geographical referencing
Registration: geographically or nongeographically (no coordination system)
Image to Map (or Ground Geocorrection)
The correction of digital images to ground coordinates using ground control points collected from maps (Topographic map, DLG) or ground GPS points.
Image to Image Geocorrection
Image to Image correction involves matching the coordinate systems or column and row systems of two digital images with one image acting as a reference image and the other as the image to be rectified.
Spatial interpolation: from input position to output position or coordinates.
RST (rotation, scale, and transformation), Polynomial, Triangulation
Root Mean Square Error (RMS): The RMS is the error term used to determine the accuracy of the transformation from one system to another. It is the difference between the desired output coordinate for a GCP and the actual.
Intensity (or pixel value) interpolation (also called resampling): The process of extrapolating data values to a new grid, and is the step in rectifying an image that calculates pixel values for the rectified grid from the original data grid.
Nearest neighbor, Bilinear, Cubic

31. Image enhancement 4 image reduction, image magnification,
transect extraction,
contrast adjustments (linear and non-linear),
band ratioing,
spatial filtering,
fourier transformations,
principle components analysis,
texture transformations, and
image sharpening

32. Purposes of image classification5
Purposes of image classification
Land use and land cover (LULC)
Vegetation types
Geologic terrains
Mineral exploration
Alteration mapping
…….

33. What is image classification or pattern recognition
Is a process of classifying multispectral (hyperspectral) images into patterns of varying gray or assigned colors that represent either
clusters of statistically different sets of multiband data, some of which can be correlated with separable classes/features/materials. This is the result of Unsupervised Classification, or
numerical discriminators composed of these sets of data that have been grouped and specified by associating each with a particular class, etc. whose identity is known independently and which has representative areas (training sites) within the image where that class is located. This is the result of Supervised Classification.
Spectral classes are those that are inherent in the remote sensor data and must be identified and then labeled by the analyst.
Information classes are those that human beings define.

34. supervised classification
supervised classification. Identify known a priori through a combination of fieldwork, map analysis, and personal experience as training sites; the spectral characteristics of these sites are used to train the classification algorithm for eventual land-cover mapping of the remainder of the image. Every pixel both within and outside the training sites is then evaluated and assigned to the class of which it has the highest likelihood of being a member.
unsupervised classification, The computer or algorithm automatically group pixels with similar spectral characteristics (means, standard deviations, covariance matrices, correlation matrices, etc.) into unique clusters according to some statistically determined criteria. The analyst then re-labels and combines the spectral clusters into information classes.

35. Hard vs. Fuzzy classification
Supervised and unsupervised classification algorithms typically use hard classification logic to produce a classification map that consists of hard, discrete categories (e.g., forest, agriculture).
Conversely, it is also possible to use fuzzy set classification logic, which takes into account the heterogeneous and imprecise nature (mix pixels) of the real world. Proportion of the m classes within a pixel (e.g., 10% bare soil, 10% shrub, 80% forest). Fuzzy classification schemes are not currently standardized.

37. Pixel-based vs. Object-oriented classification
In the past, most digital image classification was based on processing the entire scene pixel by pixel. This is commonly referred to as per-pixel (pixel-based) classification.
Object-oriented classification techniques allow the analyst to decompose the scene into many relatively homogenous image objects (referred to as patches or segments) using a multi-resolution image segmentation process. The various statistical characteristics of these homogeneous image objects in the scene are then subjected to traditional statistical or fuzzy logic classification. Object-oriented classification based on image segmentation is often used for the analysis of high-spatial-resolution imagery (e.g., 1  1 m Space Imaging IKONOS and 0.61  0.61 m Digital Globe QuickBird).

38. Unsupervised classification
Uses statistical techniques to group n-dimensional data into their natural spectral clusters, and uses the iterative procedures
label certain clusters as specific information classes
K-mean and ISODATA
For the first iteration arbitrary starting values (i.e., the cluster properties) have to be selected. These initial values can influence the outcome of the classification.
In general, both methods assign first arbitrary initial cluster values. The second step classifies each pixel to the closest cluster. In the third step the new cluster mean vectors are calculated based on all the pixels in one cluster. The second and third steps are repeated until the "change" between the iteration is small. The "change" can be defined in several different ways, either by measuring the distances of the mean cluster vector have changed from one iteration to another or by the percentage of pixels that have changed between iterations.
The ISODATA algorithm has some further refinements by splitting and merging of clusters. Clusters are merged if either the number of members (pixel) in a cluster is less than a certain threshold or if the centers of two clusters are closer than a certain threshold. Clusters are split into two different clusters if the cluster standard deviation exceeds a predefined value and the number of members (pixels) is twice the threshold for the minimum number of members.

40. Supervised classification: training sites selection
Based on known a priori through a combination of fieldwork, map analysis, and personal experience
on-screen selection of polygonal training data (ROI), and/or
on-screen seeding of training data (ENVI does not have this, Erdas Imagine does).
The seed program begins at a single x, y location and evaluates neighboring pixel values in all bands of interest. Using criteria specified by the analyst, the seed algorithm expands outward like an amoeba as long as it finds pixels with spectral characteristics similar to the original seed pixel. This is a very effective way of collecting homogeneous training information.
From spectral library of field measurements

41. Selecting
ROIs
Alfalfa
Cotton
Grass
Fallow

42. Supervised classification methods
Various supervised classification algorithms may be used to assign an unknown pixel to one of m possible classes. The choice of a particular classifier or decision rule depends on the nature of the input data and the desired output. Parametric classification algorithms assumes that the observed measurement vectors Xc obtained for each class in each spectral band during the training phase of the supervised classification are Gaussian; that is, they are normally distributed. Nonparametric classification algorithms make no such assumption.
Several widely adopted nonparametric classification algorithms include:
one-dimensional density slicing
parallepiped,
minimum distance,
nearest-neighbor, and
neural network and expert system analysis.
The most widely adopted parametric classification algorithms is the:
maximum likelihood.
Hyperspectral classification methods
Binary Encoding
Spectral Angle Mapper
Matched Filtering
Spectral Feature Fitting
Linear Spectral Unmixing

43. Supervised classification method: Spectral Feature Fitting
Source:
.gov/html/data.html

44. Accuracy assessment of classification6
Remote sensing-derived thematic information are becoming increasingly important. Unfortunately, they contain errors.
Errors come from 5 sources:
Geometric error still there
None of atmospheric correction is perfect
Clusters incorrectly labeled after unsupervised classification
Training sites incorrectly labeled before supervised classification
None of classification method is perfect
We should identify the sources of the error, minimize it, do accuracy assessment, create metadata before being used in scientific investigations and policy decisions.
We usually need GIS layers to assist our classification.

45. Post-classification and GIS7
salt-
and-
pepper

46. types Majority/Minority Analysis Clump Classes Morphology Filters
Sieve Classes
Combine Classes
Classification to vector (GIS)

47. 8
Change detection
Change detect involves the use of multi-temporal datasets to discriminate areas of land cover change between dates of imaging.
Ideally, it requires
Same or similar sensor, resolution, viewing geometry, spectral bands, radiomatric resolution, acquisition time of data, and anniversary dates
Accurate spatial registration (less than 0.5 pixel error)
Methods
Independently classified and registered, then compare them
Classification of combined multi-temporal datasets,
Principal components analysis of combined multi-temporal datasets
Image differencing (subtracting), (needs to find change/no change threshold, change area will be in the tails of the histogram distribution)
Image ratioing (dividing), (needs to find change/no change threshold, change area will be in the tails of the histogram distribution)
Change vector analysis
Delta transformation

48. Example: stages of development

49. Sun City – Hilton Head
1994
1996

50. 1974
1,040 urban
hectares
1994
3,263 urban
315% increase