Radiometric Correction The radiance measured by any given system over a given object is influenced by: Changes in scene illumination Atmospheric conditions Viewing geometry variations: Greater in the case of airborne data collection than in satellite image acquisition Instrument response characteristics

Radiometric Correction Sun elevation correction and earth-sun distance correction Haze compensation

Radiometric Correction Noise correction electronic noise - both random and periodic Sun-angle correction for comparison and mosaic images acquired from different time of the year Correction for atmospheric scattering subtract the haze DN values from different bands DN

Radiometric Correction Radiometric Correction addresses: Sensor and Processing effects Atmospheric effects Background Absolute correction Relative correction

Radiometric Correction Radiometric corrections may be necessary due to variations in scene illumination and viewing geometry, atmospheric conditions, and sensor noise and response. Each of these will vary depending on the specific sensor and platform used to acquire the data and the conditions during data acquisition. Also, it may be desirable to convert and/or calibrate the data to known (absolute) radiation or reflectance units to facilitate comparison between data.

Radiometric Correction Radiometric Correction addresses: Sensor and Processing effects Atmospheric effects Background Absolute correction Relative correction Topographic effects

Noise Removal Image noise is any unwanted disturbance in image data that is due to limitations in the sensing, signal digitization, or data recording process. Potential source: electronic interference between sensor components Noise can either degrade or totally mask the true radiometric information content of a digital image Noise removal usually precedes any subsequent enhancement or classification of the image data

Noise Removal The objective is to restore an image to as close an approximation of the original scene as possible Line striping or banding  destriping Line striping occurs due to non-identical detector response Although the detectors for all satellite sensors are carefully calibrated and matched before the launch of the satellite With time the response of some detectors may drift to higher or lower levels, resulting in relatively higher or lower values along every sixth line in the image data Line striping is corrected using histograms per detector

Noise Removal Line drop: Occurs due to recording problems when one of the detectors of the sensor in question either gives wrong data or stops functioning. The Landsat ETM, for example, has 16 detectors in all its bands, except the thermal band A loss of one of the detectors would result in every sixteenth scan line being a string of zeros that would plot as a black line on the image Dropped lines are normally 'corrected' by replacing the line with the pixel values in the line above or below, or with the average of the two. Detector: Component of a remote sensing system that converts electromagnetic radiation into a recorded signal Atmospheric Path Radiance Is a term that refers to that component of radiation received by a sensor that did not originate from the target but through scattering in the earth's atmosphere.

Noise Removal SAR-B radar image before and after noise removal

Striping-Landsat

Partially missing lines-Example

Striping Striping was common in early Landsat MSS data due to variations and drift in the response over time of the six MSS detectors. The 'drift' was different for each of the six detectors, causing the same brightness to be represented differently by each detector. The corrective process made a relative correction among the six sensors to bring their apparent values in line with each other

Lines Dropped Dropped lines occur when there are systems errors which result in missing or defective data along a scan line. Dropped lines are normally 'corrected' by replacing the line with the pixel values in the line above or below, or with the average of the two.

Radiometric Correction Radiometric Correction addresses: Sensor and Processing effects Atmospheric effects Background Absolute correction Relative correction Topographic effects

Radiometric Correction Direct Illumination

Radiometric Correction Direct Illumination Scattered Illumination

Radiometric Correction Direct Illumination Scattered Illumination Adjacent Reflections Path Radiance Irradiance stems from two sources: (1) directly reflected sunlight and (2) diffuse skylight, which is sunlight that has been previously scattered by the atmosphere.

Radiometric Correction

Radiometric Correction Rayleigh Scattering Rayleigh Scattering Caused by particles much smaller than a wavelength Declines with the fourth power of the wavelength Responsible for blue skies, red sunsets Key element in radiometric correction of images

Some form of radiometric correction is required so as to be able to more readily compare images collected by different sensors at different dates or times, or to mosaic multiple images from a single sensor

Radiometric Correction Radiometric Correction addresses: Sensor and Processing effects Atmospheric effects Background Absolute correction Relative correction Topographic effects

Absolute Radiometric Correction Absolute radiometric correction is an analytical approach to deriving reflectance values from raw Digital Numbers (DNs) 1. DN to Radiance 2. Radiance to apparent reflectance 3. Apparent reflectance to ground reflectance

Absolute Correction Convert DN to radiance, Lapp • Sensor dependent • Lapp=Ai*DN+Bi (Landsat) • Lapp=DN/Ai (Spot) • Ai calibration gain, Bi calibration offset • Which values Ai and Bi to use? Usually both analytical (derived from pre-launch measurements) and empirical (derived from post-launch measurements) exist MSS shows 8-12% difference ; sensor vs. ground processing (Markham and Barker, 1987)

Landsat ETM+ DN to Radiance L=gain*DN+offset L= (LMAX-LMIN/255) DN+LMIN L=Spectral radiance measured (over the spectral bandwidth of the channel) LMAX=The minimum radiance required to generate the maximum DN (here 255) LMIN=The spectral radiance corresponding to a DN response of 0 DN=Digital number value recorded G=Slope of response function (channel gain) B=Intercept of response function (channel offset)

Landsat ETM+ DN to Radiance (Radiometric response function for an individual TM channel) Lmax Gain L=Spectral radiance Lmin Offset [0,1] DN=Digital number 255

Landsat ETM+ DN to Radiance Source: ETM+ Science Data Users Handbook

Landsat 7 ETM+ DN to Radiance Lmax=297.5 Gain = ((297.5- (-6.2)) /255 Gain L=Spectral radiance Lmin=-6.2 Offset=Lmin=-6.2 [0,1] DN=Digital number 255

Atmospheric Effects Lapp=apparent radiance measured by sensor ρ = reflectance of object T = atmospheric transmittance E = irradiance on object, incoming Lp = path radiance/haze, from the atmosphere and not from the object

Sun angle correction Position of the sun relative to the earth changes depending on time of the day and the day of the year Solar elevation angle: Time- and location dependent In the northern hemisphere the solar elevation angle is smaller in winter than in summer The solar zenith angle is equal to 90 degree minus the solar elevation angle Irradiance varies with the seasonal changes in solar elevation angle and the changing distance between the earth and sun Zenith

Sun angle correction An absolute correction involves dividing the DN-value in the image data by the sine of the solar elevation angle Size of the angle is given in the header of the image data

Sun angle correction Landsat 7 ETM+ color infrared composites acquired with different sun angle. The left image was acquired with a sun elevation of 37° and right image with a sun elevation of 42°. The difference in reflectance is clearly shown. (B) The left image was corrected to meet the right image.

Spectral Irradiance & Earth-Sun Distance An astronomical unit is equivalent to the mean distance between the earth and the sun, approximately 149.6 ×106 km The irradiance from the sun decreases as the square of the earth- sun distance

Haze Reduction Aerial and satellite images often contain haze. Presence of haze reduces image contrast and makes visual examination of images difficult. Due to Rayleigh scattering Particle size responsible for effect smaller than the radiation’s wavelength (e.g. oxygen and nitrogen) Haze has an additive effect resulting in higher DN values Scattering is wavelength dependent Scattering is more pronounced in shorter wavelengths and negligible in the NIR

Haze Reduction One means of haze compensation in multispectral data is to observe the radiance recorded over target areas of zero reflectance For example, the reflectance of deep clear water is zero in NIR region of the spectrum Therefore, any signal observed over such an area represents the path radiance This value can be subtracted from all the pixels in that band

Haze Reduction Lapp=apparent radiance at sensor ρ = target reflectance T = atmospheric transmittance E = incident solar irradiance Lp = path radiance/haze

Haze Reduction (a) Before haze removal (b) After haze removal High-altitude normal color air photo of redwood stands and open grass areas in Redwood Creek Basin, California.

Haze-Example Indonesia (a) Before haze removal (b) After haze removal

Haze removal (a) The aerial image before haze removal (b) The aerial image after haze removal