Radiometric Correction

Radiometric Aspects Of Remotely Sensed Data

Compiled by: Oluwafemi Opaleye

Contents

• Introduction

• Pre-processing

• Radiometric errors– System/Sensor corrections– Atmospheric corrections

Pre-Processing

• Pre-processing consist of those operations that prepare data for subsequent analysis which attempts to compensate for systematic errors.

• The digital imageries are subjected to several corrections such as geometric, radiometric and atmospheric, though all these correction might not be necessarily be applied in all cases.

• The errors are mostly systematic and are being corrected before the images reach the users.

• Pre-processing refers to those operations that are preliminary to the main analysis.

• The operations may involve removal of unwanted and distracting elements such as image noise.

• Removal of these effects from the digital data are said to be “restored to their correct or original condition.

• Though attempts to correct data, may also introduce errors.

• Many remote sensing datasets contain high-quality, accurate data. Unfortunately, sometimes error (or noise) is introduced into the remote sensor data by:

– the environment (e.g., atmospheric scattering, cloud),

– systematic malfunction of the remote sensing system (e.g., an uncalibrated detector creates striping)

Image Quality

• The Pixel values / DN of remotely sensed images are of valuable importance to researchers from different fields.

• In the absence of an atmosphere, the electromagnetic radiation leaving the ground will reach the orbiting sensor practically unaltered in any wavelength

• i.e. what is recorded by the satellite directly corresponds to the radiance leaving the target on Earth in the wavelength range under consideration.

• The raw image as received from the satellite has quite a number of defects suffered at the time of acquisition.

• Many of these defects, if not all, have to, be removed or corrected for before the image is subjected to further processing for different applications.

• Nowadays these corrections are typically executed (if required) at the satellite data receiving stations or image-processing centres, before reaching the final user.

• It is the corrected (i.e. pre-processed) image which is commercialized to diverse users.

• Image pre-processing (Image Restoration) thus lays emphasis on radiometric corrections and geometric transformation.

Radiometric Correction:

• removal of sensor or atmospheric 'noise',• to accurately represent ground conditions:• to correct data loss, • remove haze, enable mosaicking and comparison.

• Radiometric correction is used to modify DN values in order to account for sensor malfunctions or to adjust the values to compensate for atmospheric degradation.

• Objective is to recover the “true” radiance /DN value of the acquired image.

• Radiometric Corrections are carried out when an image data recorded by the sensors contain errors in the measured brightness values of the pixels.

• These errors are refer to as radiometric errors and can result from:

1) Instruments used to record the data : mechanical, electronical or communication failure.

2) From the effect of the atmosphere:interaction of EM energy with atmospheric constituents.

Radiometric Corrections

• Sometimes detectors are not precisely equivalent in their output characteristics, and their output changes gradually over time.

• Sometimes when the emitted or reflected electro-magnetic energy is observed by a sensor on board an aircraft or a spacecraft, the observed energy does not coincide with the energy emitted or reflected from the same object observed from a short distance.

• This is sometimes due to atmospheric conditions such as fog or aerosols, sensor's response etc. which influence the observed energy.

• This might cause a decrease in image contrast.

• By subtracting the lowest reflectance value from all the radiometric values in the data set, the “error” is reduced/eliminated.

• This process is called Haze Removal.

Sources of Radiometric Error

Detectors/ Sensor Problems

•The response to electromagnetic signal by the

detectors varies as a function of the signals

wavelength (spectral sensitivity), so also the detectors

have the smallest energy they can detect below which

nothing can be detected (photoelectric sensitivity).

• Further, the detectors do not perform equally

neither are they all active at any given time.

• Any detector is subject to random fluctuations caused by electronic or structural defects in its design, fabrication or power supply.

• Errors also occur when a detector malfunctions permanently or temporary. Due to:

– changes with time – rise in temperature– Failure

Atmospheric Effects

• The gases of the atmosphere absorb EME at specific wavelengths called absorption bands.

• Wavelengths shorter than 0.3m are completely absorbed by the ozone (03) layer in the upper atmosphere.

• Clouds consist of aerosol-sized particles of liquid water (water droplets) that absorb and scatter electromagnetic radiation (EMR) at wavelengths shorter than 0.3cm (3.0 x 103m).

• Only radiation of microwave and longer wavelengths are capable of penetrating clouds without being scattered, reflected or absorbed.

• The effect that the atmosphere has on ground signal will depend on the degree of atmospheric absorption, scatter and emission at the time and geographical location (i.e. place) of sensing.

• Radiometric distortion can be of two types:

- The relative distribution of brightness value over an image in a given band can be different to that in the ground scene.

- the relative brightness of a single pixel from band to band can be distorted compared with spectral reflectance character of the corresponding region on the ground.

Effects of Radiometric Errors

Noise

• To some extent, electronic or structural defects produce a signal even in the absence of any radiation energy.

• This is noise and it generally appears as speckles/patches or snowy appearance on an image of an otherwise uniform surface.

• Image noise is any unwanted disturbance in image data that due to limitations in the sensing and data recording process.

Example

• A noise can be detected by mutually comparing neighbouring pixel values.

• If the difference between a given pixel value and its surrounding values exceeds an analyst specified threshold the pixel is assumed to contain noise.

Solution for Noise Error

• Filtering of random noise through the use of filter kernel of various sizes.

• The noisy pixel value can then be replaced by the average of its neighbouring values. Moving window 3 x 3, 5 x 5, 7x7 or 9x9 pixel are typically used in such procedures.

Line-Dropouts

• It occurs when detector receives sudden high radiance, creating a line or partial line of data with meaningless/spurious DN.

• This can occur when a detector malfunctions permanently or temporarily.

• Line dropouts are usually corrected either by replacing the defective line by a duplicate of preceding or subsequent line, or taking average of the two.

Dropped lines (correction)

Dropped lines (correction)

Landsat ETM+, for example has 16 detectors in all its bands, a loss of one of the detector would result in every 16th scan line being a string of zeros that would plot as a black line on the image

The Image after correction and the DN values

Dropped lines (comparison)

• Line striping is far more common than the line dropouts. It occurs due to variations and drift in the response of detectors.

• Striping occurs if a detector goes out of adjustment

• 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.

• As a result, every scan line recorded by that detector is brighter or darker than the other lines.

Line Striping

• It is important to understand that a valid data are present in the defective lines, but these must be corrected to match the overall scene.

Some remote sensing software have Modeler (algorithms) to eliminate striping.

• Among these algorithms are simple along-line convolution, high-pass filtering, and forward and reverse principal component transformations (Crippen, 1989a).

• Note that the de-striped image would look similar to the original image.