Remote Sensing Image Rectification and Restoration Image Rectification
- Slides: 29
Remote Sensing Image Rectification and Restoration
Image Rectification and Restoration ► ► ► Geometric correction Radiometric correction Geometric restoration
1. Geometric Correction For raw image rectification ► For multi-date images registration ► For multi-resolution images or data layers registration ► ► Systematic distortion vs. random distortion
Skew Correction Coordinate transfer Pixel value resampling http: //rst. gsfc. nasa. gov/Intro/Part 2_15. html
Ground Control Points (GCP) Features with known locations on a map (X, Y coordinates). These are the “ground control points” ► The same features can be accurately located on the images as well (column, row numbers) ► The features must be well distributed on the map and the image ► Highway intersections are commonly used ground control points ►
Finding UTM coordinates on a map
Coordinate Transform ► Coordinate transform equations relate geometrically correct map coordinates to the distorted image coordinates x = a 0 + a 1 X + a 2 Y y = b 0 + b 1 X + b 2 Y x, y: column, row number X, Y: coordinates ► Root Mean Square Error (RMSE) = √(dx)2 + (dy)2 Calculate RMSE for all control points
Resampling ► The purpose is to assign pixel values to the empty pixels in the rectified matrix output ► Superimpose the rectified output matrix to the distorted image ► The digital number (DN) of a pixel in the output matrix is assigned based on the DN of its surrounding pixels in the distorted image
Re-sampling Methods ► Nearest neighbor resampling ► Bilinear interpolation ► Cubic convolution resampling
Nearest Neighbor Resampling ► The DN of a pixel in the output matrix is assigned as the DN of the closest pixel in the distorted image ► Advantages simple computation maintain the original values ► Disadvantage spatial offset up to 1/2 pixel
Bi-linear Interpolation Distance-weighted average of DN values of the closest 4 pixels ► Advantage output image is smoother than the nearest neighbor method ► Disadvantage alters the original DN values ►
Cubic Convolution Resampling Uses DN values of the closest 16 pixels, adjusted by distance ► Advantage smooth output image ► Disadvantage alters the original DN values ►
When to Rectify before image classification ► Rectify after image classification ►
2. Radiometric Corrections Radiometric responses differ by ► dates ► sensor types ► images ► Causes: - Illumination - Atmospheric conditions - View angle or geometry - Instrument response
Radiometric Corrections ► Sun elevation correction ► Atmospheric correction ► Conversion to absolute radiance
Sun Elevation Correction DN ► ----------------Sin (Sun elevation angle) ► Assuming the terrain is flat
Satellite Summer Zenith Solar Elevation Angles Spring / Fall Winter Tangent plane Equator
Atmospheric Correction Haze compensation The DN value of an object (e. g. , a deep clear water body) with 0 reflectance = Lp ► Subtract the DN from the entire band ►
Absolute Irradiance Conversion of DN values to absolute radiance values ► It is necessary when compare different sensors, or relate ground measurements to image data ► L = (Lmax- Lmin)/255 * DN + Lmin ►
3. Geometric Restoration Stripping ► Line-drop ► Bit errors ►
Striping ► Malfunction of a detector ► Use gray scale adjustment to correct the strips
Line Drop ► using average of the above and below lines to fill the dropped line
Bit Error Salt and pepper effect due to random error ► Use 3 x 3 or 5 x 5 moving window average to remove the noise ►
Readings ► Chapter 7
Earth-Sun Distance Correction E 0 Cosq 0 ► E = ------d 2 ► Irradiance is inversely related to the square of the earth-sun distance ► E - normalized solar irradiance ► E 0 - solar irradiance at the mean Earth-sun distance ► q 0 - sun angle from the zenith ► d - Earth-sun distance
Atmospheric Correction r. ET ► Ltot = ----- + Lp p r - reflection of target E - irradiance on the target T - transmission of atmosphere Lp - scattered path radiation
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