Intro of digital image processing. Lecture 5a. Remote Sensing Raster (Matrix) Data Format. Digital number of column 5, row 4 at band 2 is expressed as BV 5,4,2 = 105. . Image file formats. BSQ (Band Sequential Format):
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Intro of digital image processing
Remote Sensing Raster (Matrix) Data Format
Digital number of column 5, row 4 at band 2 is expressed as BV5,4,2 = 105.
Matrix notation for band 2
GEO5083: Remote Sensing Image Processing and Analysis, spring 2012
Xie et al. 2004
A Posteriori Filter
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”.
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).
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 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.
Land use and land cover (LULC)
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.
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.
Sun City – Hilton Head