Tài liệu 33 Synthetic Aperture Radar Algorithms doc

Synthetic Aperture RadarAlgorithms Clay Stewart Science Applications International Corporation Vic Larson Science Applications International Corporation 33.1 Introduction 33.2 Image Form

Clay Stewart “Synthetic Aperture Radar Algorithms.”

2000 CRC Press LLC <http://www.engnetbase.com>.

Synthetic Aperture Radar

Algorithms

Clay Stewart

Science Applications International

Corporation

Vic Larson

Science Applications International

Corporation

33.1 Introduction 33.2 Image Formation

Side-Looking Airborne Radar (SLAR) •Unfocused Synthetic

Aperture Radar •Focused Synthetic Aperture Radar

33.3 SAR Image Enhancement 33.4 Automatic Object Detection and Classification in SAR Imagery

References Further Reading and Open Research Issues

33.1 Introduction

A synthetic aperture radar (SAR) is a radar sensor that provides azimuth resolution superior to that achievable with its real beam by synthesizing a long aperture using platform motion The geometry for the production of the SAR image is shown in Fig.33.1 The SAR is used to generate an electromagnetic map of the surface of the earth from an airborne or spaceborne platform This electromagnetic map

of the surface contains information that can be used to distinguish different types of objects that make

up the surface The sensor is called a synthetic aperture radar because a synthetic aperture is used to achieve the narrow beamwidth necessary to get a high cross-range resolution In SAR imagery the two dimensions are range (perpendicular to the sensor) and cross-range (parallel to the sensor) The range resolution is achieved using a high bandwidth pulsed waveform The cross-range resolution

is achieved by making use of the forward motion of the radar platform to synthesize a long aperture giving a narrow beamwidth and high cross-range resolution The pulse returns collected along this synthetic aperture are coherently combined to create the high cross-range resolution image A SAR sensor is advantageous compared to an optical sensor because it can operate day and night through clouds, fog, and rain, as well as at very long ranges At very low nominal operating frequencies, less than 1 GHz, the radar even penetrates foliage and can image objects below the tree canopy The resolution of a SAR ground map is also not fundamentally limited by the range from the sensor to the ground If a given resolution is desired at a longer range, the synthetic aperture can simply be made longer to achieve the desired cross-range resolution

A SAR image may contain “speckle” or coherent noise because it results from coherent processing of the data This speckle noise is a common characteristic of high frequency SAR imagery and reducing speckle, or building algorithms that minimize speckle, is a major part of processing SAR imagery beyond the image formation stage Traditional techniques averaged the intensity of adjacent pixels, resulting in a smoother but lower resolution image Advanced SAR sensors can collect multiple polarimetric and/or frequency channels where each channel contains unique information about the

FIGURE 33.1: SAR imaging geometry.

surface Recent systems have also used elevation angle diversity to produce 3-D SAR images using interferometric techniques In all of these techniques, some sort of averaging is employed to reduce the speckle

The largest consumers of SAR sensors and products are the defense and intelligence communities These communities use SAR to locate and target relocatable and fixed objects Manmade objects, especially ones with sharp corners, have very bright signals in SAR imagery, making these objects particularly easy to locate with a SAR sensor A technology similar to SAR is inverse synthetic aperture radar (ISAR) which employs motion of the platform to image the target in cross-range The ISAR data can be collected from a fixed radar platform since the target motion creates the viewing angle diversity necessary to achieve a given cross-range resolution ISAR systems have been used to image ships, aircraft, and ground vehicles

In addition to the defense and intelligence applications of SAR, there are several commercial remote sensing applications Because a SAR sensor can operate day and night and in all weather, it provides the ability to collect data at regular intervals uninterrupted by natural influences This stable source

of ground mapping information is invaluable in tracking agriculture and other natural resources SAR sensors have also been used to track oil spills (oil-coated water has a different backscatter than natural water), image underground rock formations (at some frequencies the radar will penetrate some soils), track ice conditions in the Arctic, and collect digital terrain elevation data

Radar is an abbreviation for RAdio Detection And Ranging Radar was developed in the 1930s and 1940s to detect and track ships and aircraft These surveillance and tracking radars were designed

so that a target was contained in a single resolution cell The size of the resolution cell was a critical design parameter Smaller resolution cells allowed one to determine the location of a target more accurately and increased the target-to-clutter ratio, improving the ability to detect a target In the

1950s it was observed that one could map the ground (an extended target that takes up more than one resolution cell) by mounting the radar on the side of an aircraft and building a surface map from the radar returns High range resolution was achieved by using a short pulse or high bandwidth waveform The cross-range resolution was limited by the size of the antenna, with the cross-range resolution roughly proportional toR/L awhereR is the range from the sensor to the ground and

L a is the length of the antenna The physical length of the antenna was constrained, limiting the resolution In 1951, Carl Wiley of the Goodyear Aircraft Corporation noted that the reflections from two fixed targets in the antenna beam, but at different angular positions relative to the velocity vector

of the platform, could be resolved by frequency analysis of the along track (or cross-range) signal spectrum Wiley simply observed that each target had different Doppler characteristics because of its relative position to the radar platform and that one could exploit the Doppler to separate the targets The Doppler effect is, of course, the change in frequency of a signal transmitted or received from a moving platform discovered by Christian J Doppler in 1853:

f d = ν/λ

wheref d is the Doppler shift, ν is the radial velocity between the radar and target, and λ is the

radar wavelength While the Doppler effect had been used in radar processing before the 1950s to separate moving targets from stationary ground clutter, Wiley’s contribution was to discover that with a side looking airborne radar (SLAR), Doppler could be used to improve the cross-range spatial resolution of the radar Other early work on SAR was done independently of Wiley at the University

of Illinois and the University of Michigan during the 1950s The first demonstration of SAR mapping was done in 1953 by the University of Illinois by performing frequency analysis of data collected by

a radar operating at a 3-cm wavelength from a C-46 aircraft Much work has been accomplished perfecting SAR hardware and processing algorithms since the first demonstration For a much more detailed description of the history of SAR including the development of focused SAR, phase compensation techniques, calibration techniques, and autofocus, see the recent book by Curlander and McDonough [1]

Before offering a brief description of some processing approaches for forming, enhancing, and interpreting SAR imagery, we give two examples of existing SAR systems and their applications The first system is the Shuttle Imaging Radar (SIR) developed by the NASA Jet Propulsion Laboratory (JPL) and flown on several space shuttle missions This system was designed for non-military collection

of geographic data The second example is the Advanced Detection Technology Sensor (ADTS) built by the Loral Corporation for the MIT Lincoln Laboratory The ADTS sensor was designed to demonstrate the capability of a SAR to detect and classify military targets Table33.1contains the basic parameters for the ADTS and SIR SAR systems along with details on several other SAR systems Figure33.2shows an example image formed from data collected by the SIR SAR The JPL engineers describe this image as follows:

This is a radar image of Mount Rainier in Washington state This image was acquired

by the Spaceborne Imaging Radar-C and X-band Synthetic Aperture Radar (SIR-C/X-SAR) aboard the space shuttle Endeavor on its 20th orbit on October 1, 1994 The area shown in the image is approximately 59 kilometers by 60 kilometers (36.5 miles by 37 miles) North is toward the top left of the image, which was composed by assigning red and green colors to the L-band, horizontally transmitted and vertically received, and the L-band, horizontally transmitted and vertically received Blue indicates the C-band, horizontally transmitted and vertically received In addition to highlighting topographic slopes facing the space shuttle, SIR-C records rugged areas as brighter and smooth areas

as darker The scene was illuminated by the shuttle’s radar from the northwest so that northwest-facing slopes are brighter and southeast-facing slopes are dark Forested regions are pale green in color; clear cuts and bare ground are bluish or purple; ice is

TABLE 33.1 Example SAR Systems

Resolution Swath Platform Bands polarization (m) width Interferometry JPL AIRSAR C, L, P–Full 4 10–18 km Cross track L,C

Along track L,C SIR-C/X-SAR C, L–Full, X - VV 30 × 30 15–90 Multi-pass ERIM IFSARE X–HH 2.5 × 0.8 10 km Cross track ERIM DCS X–Full < 1 1 km Cross track MIT LL ADTS Ka (33 GHz)–Full 0.33 400 m Multi-pass NORDEN G11 Ku–VV 1,3 5 km 3 Along track

3 Cross track Phase centers SRI UWB 100–300 MHz, 1 × 1 400–600 m None FOLPEN 2 200–400 MHz,

300–500 MHz, HH LORAL UHF 500–800 MHz, 0.6 × 0.6 280 m None

NAWC P-3 C, L, X–Full 1.5 × 0.7 5 km Along track X,C NAWC P-3 600 MHz–Full 0.33 × 0.66 930 km None UWB Upgrade tunable over 200–

900 MHz Tier II+ UAV X 1 and 0.3 10 km None SAR

dark green and white The round cone at the center of the image is the 14,435-foot (4,399-meter) active volcano, Mount Rainier On the lower slopes is a zone of rock ridges and rubble (purple to reddish) above coniferous forests (in yellow/green) The western boundary of Mount Rainier National Park is seen as a transition from protected, old-growth forest to heavily logged private land, a mosaic of recent clear cuts (bright purple/blue) and partially regrown timber plantations (pale blue)

Figure33.3is an example image collected by the ADTS system The ADTS system operates at a nominal frequency of 33 GHz and collects fully polarimetric, 1-ft resolution data This image was formed using the polarimetric whitening filter (PWF) combination of three polarimetric channels

to reduce the speckle noise The output of the PWF is an estimate of radar backscatter intensity The image displayed in Fig.33.3is based on a false color map which maps low intensity to black followed

by green, yellow, and finally white The color map simply gives the non-color radar sensor output false colors that make the low intensity shadows look black, the grass look green, the trees look yellow, and bright objects look white This sample image was collected near Stockbridge, New York, and is

of a house with an above ground swimming pool and several junked cars in the backyard The radar

is at the top of the image looking down at a 20◦depression angle The scene contains large areas of grass or crops and some foliage Note the bright returns from the manmade objects, including the circular above-ground swimming pool, and strong corner reflector scattering from some of the cars

in the backyard Also note the relatively strong return from the foliage canopy At this frequency the radar does not penetrate the foliage canopy Note the shadows behind the trees where there is no radar illumination

In this chapter on SAR algorithms, we give a brief introduction to the image formation process

in Section33.2 We review a few simple algorithms for reducing speckle noise in SAR imagery and automatic detection of manmade objects in Section33.3 We review a few simple automatic object classification algorithms for SAR imagery in Section33.4 This brief introduction to SAR only contains a few example algorithms In the Section “Further Reading”, we recommend some starting

FIGURE 33.2: SAR image of Mt Rainier in Washington State taken from shuttle imaging radar.

points for further reading on SAR algorithms, and discuss several open issues under current research

in the SAR community

33.2 Image Formation

In this section, we discuss some basic principles of SAR image formation For more detailed infor-mation about SAR image forinfor-mation, the reader is directed to the references given at the end of this chapter One fundamental scenario under which SAR data is collected is shown in Fig.33.1 An aircraft flies in a straight path at a constant velocity and collects radar data at a boresight of 90◦ In practice it is impossible for an aircraft to fly in a perfectly straight line at a constant velocity (at least within a wavelength), so motion (phase) compensation of the received radar signal is needed to ac-count for aircraft perturbations The radar on the aircraft transmits a short pulsed waveform or uses frequency modulation to achieve high range resolution imaging of the surface The pulses collected from several positions along the trajectory of the aircraft are coherently combined to synthesize a long synthetic aperture in order to achieve a high cross-range resolution on the surface In this section,

we first discuss SLAR where only range processing is performed Next, we discuss unfocused SAR where both range and cross-range processing are executed Finally, we discuss focused SAR where

“focusing” is performed in addition to range and cross-range processing to achieve the highest reso-lution and best image quality At the end of this section we briefly mention several other important

FIGURE 33.3: SAR image near Stockbridge, New York, collected by the ADTS.

SAR image formation topics such as phase compensation, clutter-lock, autofocus, spotlight SAR, and ISAR The details of these topics can be found in [1]–[3]

33.2.1 Side-Looking Airborne Radar (SLAR)

SLAR is the earliest radar system for remote surveillance of a surface These radar systems could only perform range processing to form the 2-D reflectivity map of the surface, so the cross-range resolution is limited by the real antenna beamwidth These SLAR systems typically operated at high frequencies (microwave or millimeter-wave) to maximize the cross-range resolution We cover SLAR systems because SLAR performs the same range processing as SAR, and the limitations of a SLAR motivate the need for SAR processing

The resolution of a SLAR system is limited by the radar pulse width in the range dimension, and the beamwidth and slant range in the cross-range dimension:

δ r = cT /2 cos η

δ cr = Rλ/L a

where we represent the approximate 3-dB beamwidth of the antenna byλ/L a , δ ris the range resolu-tion,δ cris the cross-range resolution,c is the speed of wave propagation, T is the compressed pulse

width,η is the angle between the radar beam and the surface, R is the slant range to the surface, λ is

the wavelength, andL ais the length of the antenna

The goal is to design the SLAR with a narrow beamwidth, short slant range, and a short pulsewidth

to achieve high resolution In practice, the pulsewidth of the radar is limited by hardware constraints and the amount of “energy on target” required to get sufficient signal-to-noise ratio to obtain a good

image To achieve a high range resolution without a short pulse, frequency modulation can be used

to synthesize an effectively short pulse This process of generating a narrow synthetic pulsewidth is called pulse compression The approach is to introduce a modulation on the transmitted pulse, and then pass the received signal through a filter matched to the transmit signal modulation The most common transmit waveforms used for pulse compression are linear FM (or chirp) and phase coded Some radars use a digital version of linear FM called a stepped frequency waveform

We illustrate pulse compression with the ideal application of the linear FM waveform The square pulse is modulated by a linear FM signal, and the resulting transmit signal is

s(t) =





 cos



ω0†−1

2µ†



|†| ≤ T /2

where the bandwidth (frequency deviation) introduced by the linear FM is

1f = T µ/2π

If this transmit pulse is perfectly reflected from a stationary point target, range losses are ignored, and

we shift in time to remove the two-way delay; the received signal is exactly the same as the transmitted signal The matched filter response for the transmitted signal is

h(t) =



2µ π

1/2

cos



ω0†+1

2µ†



The output of the received signal applied to the matched filter is:

9(†) =



µT2

2π

1/2sin(µT†/2) (µT†/2) Re



e j



ω0 † + 1µ†2+π/4

This output has a mainlobe that has a 4-dB beamwidth of 1/1f The resulting compressed pulse

can be significantly narrower than the width of the transmitted pulse with a pulse compression ratio

ofT 1f The range resolution of the radar has been increased by this pulse compression factor and

is now given by:

δ r ≈ c/21f cos η

Note that the range resolution in the ideal case is now completely independent of the physical width

of the transmitted pulse Performing range compression against real radar targets that Doppler shift the frequency of the receive signal introduces ambiguities resulting in additional signal processing issues that must be addressed There is a trade-off between the ability of a radar waveform to resolve

a target in range and frequency The performance of a waveform in range-frequency space is given

by its ambiguity The ambiguity function is the output of the matched filter for the signal for which

it is matched and for frequency shifted versions of that signal The references contain a much more detailed description of ambiguity functions and radar waveform design

Using pulse compression, a SLAR system can achieve a very high range resolution on the order

of 1 ft or less, but the cross-range resolution of the SLAR is limited by the physical beamwidth of the antenna, the operating frequency, and the slant range This cross-range resolution limitation of SLAR motivates the use of a synthetic array antenna to increase the cross-range resolution

33.2.2 Unfocused Synthetic Aperture Radar

Figure33.1provides a good geometric description of SAR As with SLAR, the radar platform moves along a straight line collecting radar data from the surface The SAR system goes one step further than

SLAR by coherently combining pulses collected along the flight path to synthesize a long synthetic array The beamwidth of this synthetic aperture is significantly narrower than the physical beamwidth (real beam) of the real antenna The ideal synthetic beamwidth of this synthetic aperture is

θ B = λ/2L θ

The factor of two results from the two-way propagation from the moving platform The unfocused SAR can be implemented by performing FFT processing in the cross-range dimension for the samples

in each range bin This is simply the conventional beamformer for an array antenna The difference between SAR and real beam radar is that the aperture samples that comprise the SAR are collected

at different times by a moving platform There are several design constraints on a SAR system, including:

• The speed of the platform and pulse repetition rate (PRF) of the radar must be mutually selected so that the sample points of the synthetic array are separated by less thanλ/2 to

avoid grating lobes

• The PRF must be selected so that the swath width is unambiguously sampled

• A point on the ground must be visible to the radar real beam across the entire length of the synthetic array This limits the size of the real beam antenna This constraint leads to the observation that with SAR, the smaller the real-beam antenna, the better the resolution, whereas with SLAR the larger the real-beam antenna, the better the resolution

• The SAR assumes that a ground target has an isotropic signal across the collection angle

of the radar platform as it flies along the synthetic array

The resolution of the unfocused SAR is limited because the slant range to a scatterer at a fixed location

on the surface changes along the synthetic aperture If we limit the synthetic aperture to a length so that the range from every array point in the aperture to a fixed surface location differs by less than

λ/8, then the cross-range resolution of the unfocused SAR is limited to:

δ cr =pRλ/2

33.2.3 Focused Synthetic Aperture Radar

The cross-range limitation of an unfocused SAR can be removed by focusing the data, as in optics The focusing procedure for the SAR involves adjusting the phase of the received signal for every range sample in the image so that all of the points processed in cross-range through the synthetic beamformer appear to be at the same range The phase error at each range sample used to form the SAR image is

1φ = 2π λ



d2

n

R

 radiar

whered nis the cross-range distance from the beam center,R is the slant range to the point on the

ground from the beam center, andλ is the wavelength The range samples can be focused before

cross-range processing by removing this phase error from the phase history data Note that each data point has a different phase correction based on the along-track position of the sensor and the point’s range from the sensor

When focusing is performed, the resulting SAR image resolution is independent of the slant range between the sensor and ground This can be shown as follows:

δ cr = Rθ s

θ s ≈ λ

2L e andL e≈

Rλ

L a

therefore,

δ cr ≈ L a /2

The effective beamwidth of the synthetic aperture is approximatelyλ/2L ewhere the factor of two comes from the two-way propagation of the energy (the exact effective beamwidth depends on the synthetic array taper used to control sidelobes) The length of the effective aperture(L e ) is limited

by the fact that a given scatterer on the surface must be in the mainbeam of the real radar beam for every position along the synthetic aperture The result is that the resolution of the SAR when the data is focused is approximatelyL a /2.

SAR processing can also be developed by considering the Doppler of the radar signal from the surface as first done by Wiley in 1951 When the real beamwidth of the SAR is small, a point on the surface has an approximately linearly decreasing Doppler frequency as it passes through the main beam of the real SAR beamwidth This time varying Doppler frequency has been shown to be approximately:

f d (t) = 2ν2|t − t0|

λR

whereν is the velocity of the platform and t0is the time that the point scatterer is in the center

of the main beam The change in Doppler frequency as the point passes through the main beam is

2ν2T d /λR, and T dis the time that the point is in the main beam As with linear FM pulse compression, covered in Section 33.2.1, this Doppler signal can be processed through a filter to produce a higher cross-range resolution signal which is limited by the size of the real aperture just as with the synthetic antenna interpretation(δ cr = L a /2) In a modern SAR system, typically both pulse compression

(synthetic range processing) and a synthetic aperture (synthetic cross-range processing) are employed

In most cases, these transformations are separable where the range processing is referred to as “fast time” processing and the cross-range processing is referred to as “slow-time” processing

A modern SAR system requires several additional signal processing algorithms to achieve high resolution imagery In practice, the platform does not fly a straight and level path, so the phase

of the raw receive signal must be adjusted to account for aircraft perturbations, a procedure called motion compensation In addition, since it is difficult to exactly estimate the platform parameters necessary to focus the SAR image, an autofocus algorithm is used This algorithm derives the platform parameters from the raw SAR data to focus the imagery There is also an interpolation algorithm that converts from polar to rectangular formats for the imagery display Most modern SAR systems form imagery digitally using either an FFT or a bank of matched filters Typically, a SAR will operate

in either a stripmap or spotlight mode In the stripmap mode, the SAR antenna is typically pointed perpendicular to the flight path (although it may be squinted slightly to one side) A stripmap SAR keeps its antenna position fixed and collects SAR imagery along a swath to one side of the platform

A spotlight SAR can move its antenna to point at a position on the ground for a longer period of time (thus actually achieving cross-range resolutions even greater than the aperture length over two) Many SAR systems support both stripmap and spotlight modes, using the stripmap mode to cover large areas of the surface in a slightly lower resolution mode, and spotlight modes to perform very high resolution imaging of areas of high interest

33.3 SAR Image Enhancement

In this section we review a few techniques for removing speckle noise from SAR imagery Removing the speckle can make it easier to extract information from SAR imagery and improves the visual quality