A New Method for Correcting ScanSAR Scalloping Using Forests and inter SCAN Banding Employing Dynamic Filtering

Transcription

1 A New Method for Correcting ScanSAR Scalloping Using Forests and inter SCAN Banding Employing Dynamic Filtering Masanobu Shimada Japan Aerospace Exploration Agency (JAXA), Earth Observation Research Center (EORC), Sengen 2-1-1, Tsukuba, Ibaraki, Japan, , Tel: , Fax: ,

2 Objectives and problem descriptions Advantages : wide swath with shorter revisit time Disadvantages: Resolution, Artifacts 1) Periodic artifacts in the azimuth direction called scalloping, 2) Truncation noise in the azimuth direction, and 3) Banding between the two neighboring scans. 2) This paper deals with effective reduction of these artifacts.

3 Image quality issues on ScanSAR are represented by the following three artifacts in the azimuth and range directions: 1)Periodic artifacts in the azimuth direction called scalloping, 2) Truncation noise in the azimuth direction, and 3) Banding between the two neighboring scans.

4 Scalloping Residual scalloping is caused by a mismatch of the real azimuth antenna pattern (AAP) and the model AAP, while the causes for the other phenomena are based on inaccurate knowledge of Doppler centroid frequency, and modulation of noise by the AAP in regions of excessively low signal-to-noise ratio (SNR). The scalloping can be suppressed if the noise floor level or the saturation rate of the SAR data is not too high and the Doppler frequency can be accurately estimated. Bamler proposed an excellent algorithm that generates an optimum weighting function by summing the different looks in such a way as to suppress the artifact for the given AAP and multiple looking intervals. Vigneron evaluated the inverse antenna pattern method and concluded that a higher SNR successfully suppressed the scalloping.

5 The Amazon Rainforest data have uniform backscattering characteristics independent of the incidence angle and are very good reference targets for SAR calibration. They are widely accepted as the major calibration sources and are used for SAR calibrations (i.e., estimation of the range antenna pattern (RAP) and monitoring radiometric calibration accuracy and sensor stabilities). However, they have not been discussed as for either especially suppressing the scalloping or the AAP estimation. Although the causes of the scalloping were clarified and a complex but sophisticated algorithm became available, a simpler algorithm could be possible either utilizing the Amazon data or creating a correction algorithm. This is the starting point of our research. Here, we propose a new method to estimate the AAP for the ScanSAR using only Amazon Rainforest data (i.e., not using the antenna pattern measured on the ground or the one measured using the receiver on the ground during satellite passage) and the errorless multi-looking method to minimize the scalloping.

6 Azimuth ambiguity The second artifact arises from signal truncation at the edge of the frequency spectrum. This can be solved by increasing the PRF so that the Doppler bandwidth of the illuminated area can be fully covered in order to satisfy the Niquist theorem. However, the parameter selection of the PRF and the number of pulses within a burst for each beam are sometimes restricted by the SAR system (i.e., some beams of Phased-Array L-band Synthetic Aperture Radar (PALSAR) on board the Advanced Land-Observing Satellite (ALOS) suffered occasionally by prioritizing the imaging swath of 350 km with five beams rather than the image quality). Thus, we propose to apply a band-limitation method.

7 Inter-Scan banding For the third artifact (i.e., banding between scans), the representative correction method, which was developed for Radarsar-1 and ENVISAT) is to update the roll angle and the Range-Dependent Gain Corrections (RDGCs) mainly using the overlap region of the two neighboring sub-swaths under the condition that the range antenna pattern (RAP) of all of the multiple beams are given. As an alternative to this approach, we propose a dynamic balancing method that was once adopted by the JERS-1 SAR mosaicking approach and has been improved to suppress the intensity discontinuity at neighboring sub-swaths. This method equalizes the intensity locally at the overlapped region and maintains the intensity in the high SNR region (i.e., the global center of the sub-swaths in the least square sense for range and azimuth directions.)

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9 N az,k ScanSAR imaging block diagram f PRF i 1 j i 1 th burst T SCAN i f PRF f DD N az f PRF th burst v g f PRF f DD N az,k v g Unfocused burst Focused burst on the ground j i i + 1 th burst final image on the ground x x = v g T SCAN i + f PRF f DD 2 = v g T SCAN (i 1) + f PRF f DD 2 f PRF f DD N az,k j i f PRF j i 1 f DD N az,k

10 Note: All burst numbers have a zero data reception window of 12 to 1 Table 2 Look-number distribution of PALSAR/ScanSAR No. of scans Long/short burst mode Number of bursts Number of looks 3 Short 247, 356, , 9.73, Short 247, 356, 274, , 7.13, 5.44, Short 247, 356, 274, 355, , 5.35, 4.08, 5.34, Long 480, 698, , 3.94, Long 480, 698, 534, , 2.89, 2.20, Long 480, 698, 534, 696, , 2.27, 1.73, 2.21, 2.13

11 Table 4 Typical PRF (Hz) observed at WB1 5 SCAN Scan No Hokkaido Amazon Louisiana Toyama Note: Doppler bandwidths for all scans are 1700 Hz.

12 Radiometric Expression on the focused ScanSAR data P r 2 P ( k t G a X i, j )= G p ( k X i, j )G r2 λ 2 1 ρ σ 0 r ρ a ( 4π) 3 R 4 1 S a sinθ N 2 az,k N rg 2 + G p N oise N az,k N rg k X i, j k = i T SCAN v g + x i, j k x i, j = f PRF,k f DD v g 2 f PRF,k v g f DD N az,k j i,k,( j b j i,k j ) e T SCAN = N SCAN k =1 N az,k f PRF,k S r ( k X i, j )( σ 0 ) ( 4π) 3 R 4 k ( 1 S 2 k P t G ( a )sinθ P r X i, j a X i, j )G r2 λ 2 N 2 az,k N rg2 ρ r ρ a G p ( )

13 Sr,1 NL = S r Radiometric issue, Look Summation, and error expression M-1 M-2 ( X)= 1 NL 2 ()= x A %G a f PRF f DD N az f PRF S ( r x ) NL j 2 G () j =1 a x j Sr,2( X)= ( x δ,ε) 1 T SCAN %G a 2 NL S r j =1 NL 2 G a j =1 () x j () x j 2 ( x δ,ε)= 1 { 1 G a ( x δ) }1 ( ε) 2 ( 1 2ε)G a () ()+ x 2εG a ()+ x 2δ &G a x M-1 Sr,1 ( X) 1 NL NL 2ε A 1 2ε + () x + 2δ &G a 2 G a x j =1 G a () M-2 Sr,2 ( X) A 1 2ε + 2ε NL G a j =1 NL 2 G a j =1 () x () x + 2δ NL j =1 NL 2 G a j =1 &G a () x () x

14 Azimuth Antenna Pattern G a ()= x g' ( x) Kaiser Window:To limit the frequency bandwidth M 1 l =0 = a i x i W i = ( ) I 0 πα 1 ( 2k N az 1) 2 I 0 ( πα) 0 k N az 1

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16 Truncation artifact and correction Fig. 7 ScanSAR images of an area in the Amazon with a horizontal width of 130 km do not exhibit vertical stripes for either window function.

17 Truncation artifact and correction Fig. 8 ScanSAR image of an area east of Hokkaido, Japan, with a horizontal width of 130 km is depicted in two windows. The azimuth ambiguity south of the Shiretoko peninsula appearing in the red ellipse of a) is corrected by the proposed window function and thus cannot be seen in b).

18 Fig. 2 Schematic view of the SCAN-to-SCAN correction. At Step-1, the ScanSAR SAR data intensity is modeled by a quadratic equation of the slant range. In Step-2, the near range of SCAN2 is made continuous to the far range of SCAN1 with a multiplication factor. The near range of SCAN3 is made continuous to the far range of SCAN2 with a multiplication factor. Further steps will be implemented to SCAN5. In Step-3, the continuously connected line is rotated so that its center axis is aligned with that of Step-1.

19 3.1 Step-1: Gains Accumulated g l ( ) ( ) ( R)= s l +1,near R R S k, j = G k s l, far ( R)s k, j G k k l =1 ( R)= g l R ( ) 3.2 Step-2: Correction of the over/under estimation g c m ( R)= a l R l g % k C l =0 ( ) ( ) ( R)= G k R R g c 3.3 Step-3: Smoothing in azimuth k S k, j = g % C ( R) s k, j Error ( ) ( ) G ε rror = 10 log k R 10 R g c 2

20 Fig. 11 Comparison of inter SCAN banding for a PALSAR/ScanSAR image of northern Europe. Image a) is before correction; image b) is after correction.

21 Table 3 Data set used for ScanSAR analysis No. Observation Date Latitude (degrees) Longitude (degrees) 1 Nov. 24, Nov. 24, Jan. 9, Jan. 9, July 12, July 12, July 12,

22 Table 5 Gain offset by SCAN SCAN Aa (db) Ab (db) Note: Suffix ŅaÓ refers to data collected after Aug. 7, 2006, and suffix ŅbÓ refer On Aug. 7, 2006, the attenuator of the receiver was changed slightly for each data saturation rate. Table 6 Errors of the Scan-to-Scan disbanding processes Scene Average error Standard deviation Japan Amazon Amazon Sea ice Average

23 Fig. 4 Comparison of the two scalloping correction methods. Method-1 (broken line) does not correct the variation of the data over time; Method-2 (solid line) suppresses the variation of the antenna pattern. In this case, we adopted the simulation with the following parameters: =-490.2Hz/s, =1923Hz, =0.7891, =342, =0.05, and =0.0.

24 Error estimation using the following deviated antenna pattern Fig. 5 True azimuth antenna pattern (solid line) and deviated azimuth antenna pattern (broken line). The multiplicative error in the vertical antenna pattern is 5%.

25 Fig. 9 Samples of the Inter SCAN destriping process for two cases: the Amazon on the left and Japan on the right. Each has three correction curves: a thin solid line for the accumulated curve, a thin dotted curve for the calibrated curve, and a bold solid line for the final correction curve in the range direction.

26 Fig. 10 Averaged error associated with the scan-to-scan normalization for four different images.

27 Sample images produced by suing the proposed method Examples are selected from the uniform target, large variance of the scattering target, large contrasted target, and dark target Amazon Antarctica Hokkaido Sahara Desert

28 Sample images of the SCANSAR: Desert

29 Sample images of the SCANSAR:Amazon

30 Sample images of the SCANSAR:Hokkaido and O

31 Sample images of the SCANSAR:Antarctica

32 Conclusions We have proposed a new method for scalloping reductions using the optimally estimated azimuth antenna pattern for the processor and using the Amazon forest target. The second method is to adopt the total weighting method to calculate the look summation of the different scans. The third method determines the gain difference of the neighboring paths balanced in range and azimuth directions.

33 Contents of this talk 1) Objectives 2) Problems 3) Scalloping 4) Inter Scan Banding 5) Examples 6) Discussions 7) Conclusions