Characteristics and statistics of digital remote sensing imagery


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1 Characteristics and statistics of digital remote sensing imagery There are two fundamental ways to obtain digital imagery: Acquire remotely sensed imagery in an analog format (often referred to as hardcopy) and then convert it to a digital format through the process of digitization, and Acquire remotely sensed imagery already in a digital format, such as that obtained by a Landsat sensor system. 1
2 Image Digitization: Analog to Digital (A/D) Conversion Using Linear Array Scanners Image digitization is the process of turning a hardcopy analog imagery into a digital image. (e.g., a destop scanner)
3 RGB = True Color. Any addition array that collect spectral information beyond visible light will be able to generate pseudo color image. Additional array? 3
4 Panchromatic Blac & White Infrared Truecolor vs. Pseudo (false)color 4
5 With all images are digital, nowledge and sills in digital image processing are necessary. Digital Image With raster data structure, each image is treated as an array of values of the pixels. Image data is organized as rows and columns (or lines and pixels) start from the upper left corner of the image. Each pixel (picture element) is treated as a separate unite. 5
6 Statistics of Digital Images: Looing at the frequency of occurrence of individual brightness values in the image displayed in a histogram Viewing individual pixel brightness values at specific locations or within a geographic area; Computing univariate descriptive statistics to determine if there are unusual anomalies in the image data; and Computing multivariate statistics to determine the amount of betweenband correlation (e.g., to identify redundancy). Statistics of Digital Images It is useful to calculate fundamental univariate and multivariate statistics of the multispectral remote sensor data. This normally involved computing of maximum and minimum value for each band of imagery, the range, mean, standard deviation, betweenband variancecovariance matrix, correlation matrix, and frequencies of brightness values for each band, which are used to produce histograms. Such statistics provide valuable information necessary for processing and analyzing remote sensing data. 6
7 Digital Images: A population is an infinite or finite set of elements. A sample is a subset of the elements taen from a population used to mae inferences about certain characteristics of the population. (e.g., training signatures) Large samples drawn randomly from natural populations usually produce a symmetrical frequency distribution. Most values are clustered around some central value, and the frequency of occurrence declines away from this central point. A graph of the distribution appears bell shaped is called a normal distribution. 7
8 Histogram and Its Significance to Digital Remote Sensing Image Processing Many statistical tests used in the analysis of remotely sensed data assume that the brightness values recorded in a scene are normally distributed. Unfortunately, remotely sensed data may not be normally distributed. In such instance, nonparametric statistical theory may be preferred. Forest Water 8
9 Histogram and Its Significance to Digital Remote Sensing Image Processing The histogram is a useful graphic representation of the information content of a remote sensing image, which provide readers with an appreciation of the quality of the original data, e.g. whether it is low in contrast, high in contrast, or multimodal in nature. Univariate Descriptive Image Statistics Measures of Central Tendency in Remote Sensor Data Mode: is the value that occurs most frequently in a distribution and is usually the highest point on the curve. Multiple modes are common in image dataset. Median: is the value midway in the frequency distribution. Mean: is the arithmetic average and if defined as the sum of all observations divided by the number of observations. 9
10 Measures of Central Tendency in Remote Sensor Data The mean is the arithmetic average and is defined as the sum of all brightness value observations divided by the number of observations. It is the most commonly used measure of central tendency. The mean ( ) of a single band of imagery composed of n brightness values (BV i ) is computed using the formula: Sample mean is a poor measure of central tendency when the set of observations is sewed or contains an outlier. 9 Further measurements are needed. Outlier Mean = 7/9 = 3 (Does not represent the dataset well) Sewed Mean = 9/9 = 1 Mean = 9/9 = 1 10
11 Measures of Dispersion Measures of the dispersion about the mean of a distribution provide valuable information about the image. For example, the range of a band of imagery (range ) is computed as the difference between the maximum (max ) and minimum (min ) values: Measures of Dispersion Unfortunately, when the minimum or maximum values are extreme or unusual observations, the range could be a misleading measure of dispersion. Such extreme values are not uncommon because the remote sensor data are often collected by detector systems with delicate electronics that can experience spies in voltage and other unfortunate malfunctions. When unusual values are not encountered, the range is a very important statistic often used in image enhancement functions such as min max contrast stretching. 11
12 Measures of Dispersion Variance: is the average squared deviation of all possible observations from the sample mean. The variance of a band of imagery, var, is computed using the equation: BV 11 BV 1 BV 13 BV 1 BV BV 3 BV 31 BV 3 BV 33 var n ( i 1 BV n i 1 ) BV 11 BV 1 BV 13 BV 1 BV BV 3 var n ( i 1 BV i n 1 ) BV 31 BV 3 BV 33 var 9 i 1 (1 1 ) var ( n i 1 54 ( BV i ) n 1 (1 (1 (18 (1 8 (13 ( (14 (1 1
13 Standard Deviation (s ): is the positive square root of the variance. s var A small s. suggests that observations clustered tightly around a central value. A large s indicates that values are scattered widely about the mean. The sample having the largest variance or standard deviation has the greater spread among the values of the observations. Mean Standard Deviations Standard Deviation (s ): is the positive square root of the variance. s var var ( n i 1 54 ( BV i n 1 (1 ) (1 (18 (1 8 (13 (1 (14 (1 s var
14 Measures of Distribution (Histogram) Asymmetry and Pea Sharpness Sewness is a measure of the asymmetry of a histogram and is computed using the formula: BV 11 BV 1 BV 13 BV 1 BV BV 3 BV 31 BV 3 BV 33 A perfectly symmetric histogram has a sewness value of zero. Histogram of A Single Band of Landsat Thematic Mapper Data Max. = 10 Min. = 6 Mean = 7 Median = 5 Mode = 9 14
15 Histogram of Thermal Infrared Imagery of a Thermal Plume in the Savannah River Max. = 188 Min. = 38 Mean = 73 Median = 68 Mode = 75 Remote Sensing Multivariate Statistics Remote sensing is often concerned with the measurement of how much radiant flux is reflected or emitted from an object in more than one band (e.g., in red and nearinfrared bands). It is useful to compute multivariate statistical measures such as covariance and correlation among the several bands to determine how the measurements covary. 15
16 Remote Sensing Multivariate Statistics The different remotesensingderived spectral measurements for each pixel often change together in some predictable fashion. Landsat7 ETM+ Band 1 (Blue band) Landsat7 ETM+ Band (Green band) Remote Sensing Multivariate Statistics 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; i.e., an increase or decrease in one band s brightness value is not accompanied by a predictable change in another band s brightness value. Landsat7 ETM+ Band 3 (Red band) Landsat7 ETM+ Band 4 (Near IR band) 16
17 Correlation between Multiple Bands of Remotely Sensed Data To estimate the degree of interrelation between variables in a manner not influenced by measurement units, the correlation coefficient, r, is commonly used. The correlation between two bands of remotely sensed data, r l, is the ratio of their covariance (cov l ) to the product of their standard deviations (s s l ); thus: r l cov s s l l Correlation Coefficient: A correlation coefficient of +1 indicates a positive, perfect relationship between the brightness values of the two bands. A correlation coefficient of 1 indicates that the two bands are inversely related. A correlation coefficient of zero suggests that there is no linear relationship between the two bands of data. 17
18 Remote Sensing Multivariate Statistics 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. To calculate covariance, we first compute the corrected sum of products (SP) defined by the equation: 18
19 Remote Sensing Multivariate Statistics It is computationally more efficient to use the following formula to arrive at the same result: This quantity is called the uncorrected sum of products. Remote Sensing Multivariate Statistics Covariance is calculated by dividing SP by (n 1). Therefore, the covariance between brightness values in bands and l, cov l, is equal to: 19
20 Covariance: is the joint variation of two variables about their common mean. SP l is the corrected Sum of Products between bands and l. SP cov l l n 1 SP l n ( i 1 BV i )( BV il l ) Band = Band l l = 1 SP l = (199)x(11)+(19)x(11)+(19)x(11)+(189)x(11)+(19)x(11)+(139)x(11)+ (19)x(11)+(149)x(11)+(19)x(11) = 0 Cov l = 0 Covariance: A more efficient formula is: n SP l ( BV i BV il ) i 1 n BV i 1 n i BV i 1 n il Band Band l Total = Total = 9 SP l = [(19x1)+(1x1)+(1x1)+(18x1)+(1x1)+(13x1)+(1x1)+(14x1)+(1x1)] [91x9/9] = 0 Cov l = 0 The sums of products (SP) and sums of squares (SS) can be computed for all possible band combinations. 0
21 Format of a VarianceCovariance Matrix Band 1 (green) Band (red) Band 3 (nearinfrared) Band 4 (nearinfrared) Band 1 SS 1 cov 1, cov 1,3 cov 1,4 Band cov,1 SS cov,3 cov,4 Band 3 cov 3,1 cov 3, SS 3 cov 3,4 Band 4 cov 4,1 cov 4, cov 4,3 SS 4 1
22 Remote Sensing Multivariate Statistics Variance covariance and correlation matrices are the ey for principal components analysis (PCA), feature selection, classification and accuracy assessment.
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