Spectrum Reflectance Curve PDF

Spectrum reflectance curve/signature Image resolution Resolutions determine the quality of the image and its application Three type of image resolution exist Spatial resolution Spectral resolution Radiometric resolution Spatial resolution Spatial resolution is the resolving power of an...

Spectrum reflectance curve/signature Image resolution Resolutions determine the quality of the image and its application Three type of image resolution exist Spatial resolution Spectral resolution Radiometric resolution Spatial resolution Spatial resolution is the resolving power of an instrument needed for the discrimination of features and is based on detector size, focal length, and sensor altitude. More commonly used descriptive terms for spatial resolution are Ground Sample Distance (GSD) and IFOV. The IFOV, a synonym for pixel size, is the area of terrain or ocean covered by the Field of View (FOV) of a single detector. Spectral resolution Band width within which the sensor operate E.g., Band Band (Wavelength (μm)) Resolution (m) 1 (0.450 – 0.515) Blue 30 2 (0.525 – 0.605) Green 30 3 (0.630 – 0.690) Red 30 Radiometric resolution Brightness level at the sensor, measured in form of “bit” For 8 bit image the brightness level ranges between 0 to 255, with 256 brightness level or grey level. The more the brightness level, the better is the contrast in the image and ability to separate between different colors Multispectral images Images taken at several spectrums (Bands) Depending on the sensors, the spectrum can range from 3 to 10 For Landsat 7 ETM+ there are 8 bands or spectrums For Landsat 8 & 9 (OLI & TIRS) there are 10 bands Operational Land Imager (OLI) & Thermal Infrared Sensor (TIRS) Landsat bands are as follows Satellite Sensor Landsat 7 ETM+ Band Band (Wavelength (μm)) Resolution (m) 1 (0.450 – 0.515) Blue 30 2 (0.525 – 0.605) Green 30 3 (0.630 – 0.690) Red 30 4 (0.775 – 0.900) NIR 30 5 (1.550 – 1.750) SWIR-1 30 6 (10.40 – 12.50) TIR 602 7 (2.080 – 2.350) SWIR-2 30 8 (0.520 – 0.900) Pan 15 Landsat 8-9 OLI 1 (0.435 – 0.451) Costal 30 2 (0.452 – 0.512) Blue 30 3 (0.533 – 0.590) Red 30 4 (0.636 – 0.673) NIR 30 5 (0.851 – 0.879) SWIR-1 30 6 (1.566 – 1.651) TIR 30 7 (2.107 – 2.294) SWIR-2 30 8 (0.503 – 0.676) Pan 15 9 (1.363 – 1.384) Cirrus (cloud detection) 30 TIRS 10 (10.60 – 11.19) Thermal 1002 11 (11.50 – 12.51) Thermal 1002  Image processing Image processing are ways of making the remote sensing image usable. This can range from image enhancing, to image calibration Remote sensing images comes in raw form called digital number (DN) DN images contain errors from the atmosphere, and the sensors DN images should be converted into radiance (energy) and reflectance, that provide the true representation of the object (target) under investigation The conversion of DN into radiance and reflectance is called image calibration How to correct image quality hazing cloud cover blurred image files CPF(details of the sensor) Metadata file(expam bender)...........Geo TIF