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Canada Centre for Remote Sensing, Natural Resources Canada Natural Resources Ressources naturelles Canada Canada MICROWAVE IMAGE RADIOMETRY Introduction to Fading and Speckle Fading and speckle are the inherent “noise-like” processes that occur in a coherent imaging system that preserves the phase o...

Canada Centre for Remote Sensing, Natural Resources Canada Natural Resources Ressources naturelles Canada Canada MICROWAVE IMAGE RADIOMETRY Introduction to Fading and Speckle Fading and speckle are the inherent “noise-like” processes that occur in a coherent imaging system that preserves the phase of the received signal; Coherence is the fixed relationship between waves in a beam of electromagnetic (EM) radiation. Two wave trains of EM radiation are coherent when they are in phase. That is, they vibrate in unison; Fading is due to variation in the phase delay caused by multiple targets in a resolution cell with range variations differing by less than a wavelength; Local constructive and destructive interference appears in the image as bright and dark speckles, respectively (See slide 3). Canada Centre for Remote Sensing, Natural Resources Canada Speckle Constructive Interference Result Coherent radar waves Destructive Interference Result Example of Homogenous Target Constructive interference V arying degrees of interference (between constructive and destructive ) Canada Centre for Remote Sensing, Natural Resources Canada Destructive interference Introduction to Fading and Speckle All radar images appear with some degree of what we call radar speckle and is the dominating factor in radar imagery. Speckle appears as a grainy "salt and pepper" texture in an image; It is a granular noise that inherently exists in SAR imagery (Slide 5). Speckles reduce the ability to separate land use classes of various types based on texture and radiometric information This is caused by random constructive and destructive interference from the multiple scattering returns that will occur within each resolution cell; As an example, an homogeneous target, such as a large grass-covered field, without the effects of speckle would generally result in light-toned pixel values on an image (A). However, reflections from the individual blades of grass within each resolution cell results in some image pixels being brighter and some being darker than the average tone (B), such that the field appears speckled (Slide 6). Canada Centre for Remote Sensing, Natural Resources Canada Example of Speckle Canada Centre for Remote Sensing, Natural Resources Canada Introduction to Speckle Canada Centre for Remote Sensing, Natural Resources Canada Introduction to Fading and Speckle Speckle degrade he quality of images, thus interpretation (visual or digital) is more difficult. Thus, it is generally desirable to reduce speckle prior to interpretation and analysis. Averaging independent samples, can effectively reduce the effects of fading and speckle. Therefore, speckle reduction can be achieved in two ways: Multiple-look filtering refers to the division of the radar beam (A) into several (in this example, five) narrower sub-beams (1 to 5). Each sub-beam provides an independent "look" at the illuminated scene, as the name suggests. Each of these "looks" will also be subject to speckle, but by summing and averaging them together to form the final output image, the amount of speckle will be reduced. Canada Centre for Remote Sensing, Natural Resources Canada Introduction to Fading and Speckle Averaging (incoherently) adjacent pixels: Speckle reduction filtering consists of moving a small window of a few pixels in dimension (e.g. 3x3 or 5x5) over each pixel in the image, applying a mathematical calculation using the pixel values under that window (e.g. calculating the average), and replacing the central pixel with the new value. The window is moved along in both the row and column dimensions one pixel at a time, until the entire image has been covered. By calculating the average of a small window around each pixel, a smoothing effect is achieved and the visual appearance of the speckle is reduced. Canada Centre for Remote Sensing, Natural Resources Canada Introduction to Fading and Speckle  Filtering reduces speckle at the expense of resolution, since is essentially smoothens the image;  Therefore, the amount of speckle reduction desired must be balanced with the particular application the image is being used for, and the amount of detail required;  If fine detail and high resolution is required then little or no multi-looking/spatial filtering should be done. If broad-scale interpretation and mapping is the application, then speckle reduction techniques may be more appropriate and acceptable.  While multi-looking is usually done during data acquisition, speckle reduction by spatial filtering is performed on the output image in a digital (i.e. computer) image analysis environment. Canada Centre for Remote Sensing, Natural Resources Canada  Reducing these effects enhances radiometric resolution at the expense of spatial resolution. LOOK Multi-Looking Concept - Each of the sub-images used to form the output summed image implemented in the processor. SPECKLE - Statistical fluctuation or uncertainty associated with the brightness of each pixel in a radar image due to coherent illumination and processing Single look image uses all signal returns from a ground target to create a single image; Multi looking is used to reduce speckle in the final detected image, assuming that phase is not needed, Image will contain speckle but have the highest achievable resolution Canada Centre for Remote Sensing, Natural Resources Canada SAR Image Artifacts SAR image artifacts can occur due to platform, sensor, and/or processing problems ◆ Ambiguities - Azimuth Ambiguity - Range Ambiguity - Nadir Ambiguity ◆ Scalloping ◆ Automatic Gain Control effects for RADARSAT-1 Image radiometrics & geometrics can be affected Sometimes reprocessing can improve Sometimes incorrigible Canada Centre for Remote Sensing, Natural Resources Canada Ambiguities Copy of target appears offset in range and/or in azimuth (ghosting) Artifacts visible if background is dark and invariant (e.g. calm water), difficult to detect over variable background (e.g. forested land) Desired signal is contaminated by signal of adjacent targets Canada Centre for Remote Sensing, Natural Resources Canada Ambiguities Azimuth Ambiguity ◆ Halifax Harbour, Nova Scotia Ghost fleet of ships seen in RADARSAT S7 image too slow sampling of returned signals Range Ambiguity ◆ simultaneous returns from desired illuminated region and of a previously or successively transmitted pulse - e.g. Nadir Return - return from “under the satellite” accompanies return from imaged swath Source: Werle, 1997 Canada Centre for Remote Sensing, Natural Resources Canada Nadir Ambiguities These bright linear features appear at approximately constant range Signal returns from nadir are strong due to near-specular reflection from targets within a very narrow slant range distancebright tone Due to pulse compression, bright return is restricted to a small number of range cells sharp, linear shape Canada Centre for Remote Sensing, Natural Resources Canada Scalloping Caused by improper estimation of Doppler Centroid Seen as corduroy-like radiometric banding across the scene (range direction) Occasionally visible in RADARSAT ScanSAR mode products Image can be reprocessed using better Doppler Centroid estimates Canada Centre for Remote Sensing, Natural Resources Canada SAR Image Interpretation -Outline- Elements of interpretation ◆ Tone ◆ Texture Canada Centre for Remote Sensing, Natural Resources Canada Image Brightness Variations and Interpretation Two major types of brightness variations observable in a radar image: ◆ variations in tone ◆ variations in texture Though uncommon, radar artifacts are a potential source of unwanted brightness variation as well Computers are used to supplement and/or extend our visual interpretation of these brightness variations Canada Centre for Remote Sensing, Natural Resources Canada Elements of Interpretation Interpretation Element Example of computer interpretation technique tone colour texture pattern size shape association density slicing multispectral classification texture analysis spatial transforms / classification size feature classification syntactic classification contextual classification Source: Manual of Remote Sensing, 1983 Canada Centre for Remote Sensing, Natural Resources Canada Image Tone Refers to each distinguishable grey level from black to white Proportional to strength of radar backscatter Relatively smooth targets like calm water appear as dark tones Diffuse targets like some vegetation appear as intermediate tones Man-made targets (buildings, ships) may produce bright tones, depending on their shape, orientation and/or constituent materials Canada Centre for Remote Sensing, Natural Resources Canada Image Tone (cont’d) DARK MEDIUM Source: CCRS Canada Centre for Remote Sensing, Natural Resources Canada BRIGHT Image Texture Refers to the pattern of spatial tone variations Function of spatial uniformity of scene targets For radar images texture consists of scene texture multiplied by speckle Texture may be described as fine, medium, or coarse Canada Centre for Remote Sensing, Natural Resources Canada Image Texture (cont’d) Corn Field Spatially Uniform Target Fine Texture 300 m Source: Ulaby and Dobson, 1989 Canada Centre for Remote Sensing, Natural Resources Canada Forest Spatially Non-Uniform Target Coarse Texture 300 m