Progress In Electromagnetics Research B, Vol. 20, , 2010

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1 Progress In Eectromagnetics Research B, Vo. 20, , 2010 TARGET-AIDED SAR IMAGE INTELLIGENT COMPRESSION X.-H. Yuan, Z.-D. Zhu, and G. Zhang Coege of Information Science and Technoogy Nanjing University of Aeronautics and Astronautics Nanjing , China Abstract Inteigent compression is important to image transmission in rea time over bandimited channes for synthetic aperture radar (SAR) payoads depoyed on unmanned aeria vehices (UAV), where target areas are encoded with high fideity, whie background data are encoded with esser fideity. A target-aided SAR image inteigent compression (TAIC) system is presented in this paper, which utiizes robust fixed-rate treis-coded quantization (FRTCQ) to encode target sequences and FRTCQ to encode background sequences. Mutiresoution constant fase aarm rate (CFAR) detector in waveet domain using db4 based on the mutiscae mode of target is embedded. Generic region of interest (ROI) mask is created. In order to achieve better quaity of target areas decoded, ROI mask is modified. The improved performance using TAIC system by compressing target chips from training set and testing set in Moving and Stationary Target Acquisition and Recognition (MSTAR) database is demonstrated. 1. INTRODUCTION Synthetic aperture radar (SAR) payoads depoyed on UAV wi pay an important roe in future warfare, because these systems can operate in a weather conditions at any time of day or night and generate images of ground in rea time [1]. The miitary is interested in automatic detecting and recognizing targets from imagery [2 7] to hep image anaysts perform interpretation. UAV can direct imagery data streams to ground station and image anayst through the use of sateite communications. As sensor resoution increased, more than 1 miion pixes/s wi be produced by SAR system, but sateite communication Corresponding author: X.-H. Yuan (cedar@nuaa.edu.cn).

2 286 Yuan, Zhu, and Zhang channes to transmit image data from UAV are bandimited. For exampe, the commerciay avaiabe T 1 is ony about bit/s. Image data must be compressed on-board, then transmitted to the ground stations anaysis by automatic target recognition (ATR) system or by image interpreters to provide critica and timey information to fied commanders. Conventiona image compression agorithms introduce distortion by coding the entire image uniformy which severey degrades the ATR system s performance, not to mention the perceptua quaity of image from anayst [8]. Recent works have been devoted to devising content-based compression agorithm, where target areas are extracted by an externa on-board ATR agorithm and encoded at a higher bit rate than background with vector quantization or variabe ength entropy coding [9, 10]. However, due to the physica imitations of on-board SAR, an efficient automatic target detection agorithm incorporated into encoder is required. Furthermore, both vector quantization and entropy coding are susceptibe to channe errors. Bonneau has proposed an inteigent compression agorithm using an embedded automatic target detector in [11], but it works ony on simuated SAR image. To reaize extreme compression whie maintaining high quaity of target area for further anaysis, we investigate target-aided SAR image inteigent compression (TAIC) system based on Bonneau s. The research presented in this paper is different from others in threefod. First, we use a-pass fitering to ift up the fixed rate treis quantization (FRTCQ) performance curve of a broad cass of sources to the eve of memoryess Gaussian FRTCQ. This method is referred to as robust FRTCQ. Second, unike the method in [11] where apass fitering is impemented on whoe-image, we ony use robust FRTCQ to encode target sequences, which eads to a much ess compex impementation since targets ony occupy sma parts of SAR image. Third, we determine to use db4 waveet for the TAIC system. Generic ROI mask is generated according to the ifting steps of inverse waveet transform. In order to achieve better quaity of target areas decoded, we modify the ROI mask. It is common in practice to take ogarithm of image intensity vaue reducing the high dynamic range compatibe with the human visua system and transforming mutipicative noise into additive noise. Logdetected SAR image is concerned in this paper.

3 Progress In Eectromagnetics Research B, Vo. 20, TARGET-AIDED SAR IMAGE INTELLIGENT COMPRESSION 2.1. System Description The overa Target-aided SAR image inteigent compression system is shown in Fig. 1. High resoution SAR image is first decomposed into 22 subbands using a 2-D discrete waveet transform (DWT). A standard 4-eve dyadic decomposition is performed, with one additiona eve of decomposition being performed on the highest frequency component foowing the first decomposition eve. ROI is determined by mutiresoution detection agorithm. ROI mask is used to cassify the waveet coefficients in each subband into either a target cass or a background cass. For each subband, the DWT coefficients corresponding to the same cass are grouped into sequences; they are normaized by subtracting their mean and divided by their respective standard deviation. Then target sequences are apass fitered being neary Gaussian distribution to be encoded using FRTCQ, and codebooks are designed in one bit increments from 4 to 8 bits/sampe optimized for Gaussian distribution. The background sequences are encoded with FRTCQ directy, and codebooks are designed in one bit increments from 1 to 4 bits/sampe optimized for the Lapacian distribution, which we have found reasonabe [12]. The side information required to encode consists of the mean of sequences in owest frequency, standard deviations of a sequences, ROI ocation, size, and codebook indices. The tota side information consists of 408 bits. This corresponds to bpp for a image. High resoution SAR image targets bpp Backgroud bpp 22 subband Target Mutiresoution mode ROI mask ROIs determined by mutiresoution CFAR encoder target sequences background sequences a-pass fitering statistic normaiztion rate aocation indices for codebook side information (statistic for each sequence) side information( size position for ROI ) TCQ indices for target sequence indices for background sequence channe Figure 1. Target-aided SAR image inteigent compression system.

4 288 Yuan, Zhu, and Zhang 2.2. Robust FRTCQ Compared with many other quantization schemes, TCQ is moderatey robust to channe errors. Fig. 2 shows the obtained signa-to-noise ratio (SNR) of FRTCQ when encoding the memoryess Gaussian, Lapacian, and generaized Gaussian (with shape parameter ν = 0.5) sources at bit rates of 1 4 bits/sampe. Aso shown are the respective rate-distortion functions (R(D)). From the graph, it is evident that the encoded performance of FRTCQ is opposite to that theoreticay possibe as seen from the rate distortion functions. To address this issue, a robust quantization method is utiized where the signa to be quantized is apass fitered to produce a signa with Gaussian statistics. The a-pass fiter is impemented by using a phase scrambing operation. In this way, the performance curves of a Gaussian-optimized quantizer can be achieved with a broad range of source distributions. Thus, a fixed (Gaussian) rate-distortion performance is guaranteed, independent of the source distribution. To demonstrate the effect of a-pass fitering, Fig. 3 shows the normaized histograms of the Generaized Gaussian (ν = 0.5) 65,535 sampes data and phase-scrambing sequence. For comparison, the histogram of same size sampe data derived from a Gaussian pseudo-random number generator is aso shown. The graph justifies the resuting sequence of phase descrambed generaized Gaussian distribution (GGD) as neary Gaussian distribution. Note that the variance of the phase-scrambed GGD sequence is identica to the variance of the origina GGD sequence due to energy preserve transform [13]. SNR(dB) R(D)-GGD R(D)-Lapacian R(D)-Gaussian FTCQ-Gaussian FTCQ-Lapacian FTCQ-GGD Gaussian GGD a-pass fitering bits/sampe Figure 2. SNR performance of FRTCQ quantization for generaized Gaussian sources Figure 3. Normaized histogram of GGD (shape parameter ν = 0.5) and phase scrambed sequence.

5 Progress In Eectromagnetics Research B, Vo. 20, Tabe 1. SNR s (db) of the robust FRTCQ and FRTCQ for memoryess Generaized Gaussian Distribution sources. GGD (ν = 0.5) Gaussian Rate (bits/sampe) FRTCQ Robust FRTCQ FRTCQ The comparison between the resuting SNR performance and those from the non-robust quantizer of GGD source is shown in Tabe 1. The resuts of robust quantizer for memeoryess generaized Gaussian source at encoding rates of 1 4 bits/sampe are identica to those non-robust quantizer for the memoryess Gaussian source. The same is observed at even higher encoding rates. Impementing the a-pass fitering using the phase scrambing method can be efficienty done by using fast Fourier transforms (FFT s). An efficient M-point one-dimensiona (1-D) FFT requires about M og 2 M compex mutipications and additions. The size of the FFT s (and inverse FFT) is seected to be the same as that of target sequences. As the target ony occupies a sma part of image, robust quantization ony to target sequences can ead to a much ess compex impementation since a reativey few of coefficients need be a-pass fitered Mutiresoution CFAR Detection in Waveet Domain Conventiona compression system contains three buiding bocks: transform, quantization, and coding. ROI must be extracted before quantization in proposed compression system. Two-dimensiona discrete waveet transform is appied to a fu resoution SAR image, and ow frequency subbands LL 1, LL 2,..., LL 4 are created. Each of them is normaized by subtracting its mean vaue forming a mutiresoution waveet coefficient image sequence of I 1, I 2,..., I 4. From coarser to finer scaes, the waveet coefficient image vaues can be predicted by using its coarser-scae ancestor, and scae AR (auto-regressive) modes can be written in the form [11]. I(s) = a 1,m(s) I(s γ)+a 2,m(s) I(s γ 2 )+...+a R,m(s) (s)i(s γ R )+w(s) (1) where s has four offspring denoted sα NW, sα NE, sα SE, sα SW and one parent denoted s γ. m denotes scae, and R is the order of the

6 290 Yuan, Zhu, and Zhang regression. m(s) is the scae of s, and a 1,m(s), a 2,m(s),..., a R,m(s) are the rea, scaar-vaued regression coefficients. w(s) is white driving noise. Defining the vector a k = (a 1,k a 2,k... a R,k ), we can characterize a given object (target) in the image first by soving for the autoregressive coefficients, a 1, a 2,..., and a k by east-square approach: [ a k =arg min I(s) a1,k I(s γ)... a R,k I(s γ R ) ] 2 (2) a k R N { s m(s)=k} These regression coefficients are aowed to be scae varying, but restricted to be shift-invariant. The regression ength R may be seected in a manner to that standard AR mode in time series but imited by the eves of waveet decomposition. We can predict the target signa by using these regression coefficients, the residua between the target signa and input is w(s). w(s) = I(s) [ a 1,m(s) I(s γ) a R,m(s) I(s γ R ) ] (3) For og-detected SAR image, homogenous cutter is predominant when targets occupy sma parts, and waveet coefficients in owest frequency subband can be modeed as Gaussian distribution [14], so is the distribution of predict residua w(s). In fact, target areas in predicted residua image are brighter than remains. We now compute a test statistic based on the residua between the predicted coefficient and any given image coefficient as shown in equation ζ(s) = w(s) µ c σ c (4) where µ c is the average of the expected residua for the object mode, and σ c is the standard deviation of those coefficients. For every scae of residua image, target areas appear much brighter than remains. To make our search criteria for a given object more robust, we buid a compicated test statistic c(s) = [ ζ(s) + ζ(s γ) + ζ(s γ 2 ) ζ(s γ N 2 ) ] / N 1 (5) where N is waveet decomposition eves of 4. When the Gaussian c(s) mode is accurate, and the target occupies a sma part, threshod c(s) at vaue of T eads to a per-pixe fase aarm probabiity of P F A = 1 2 erfc ( T 2 ) (6)

7 Progress In Eectromagnetics Research B, Vo. 20, Assuming Q independent pixes per square kiometer, the corresponding fase aarm rate (FA/km 2 ) is F AR = Q 2 erfc ( T 2 ) (7) To evauate the performance of c(s) with different waveet fiters, we test it on the pubic domain MSTAR database. The database contains target and cutter SAR imagery coected at X- band waveengths. The imagery has a resoution of 1 ft in both the range and azimuth directions. The target data from three different target types BMP2, BTR70, and T72 are divided into a training database of 1622 target chips and a testing database of 1365 chips. The database consists of cutter scenes covering approximatey 10 square kiometers of ground. Both the cutter and target data are stored as magnitude detected imagery. Each pixe in the target chip fies is stored as binary 4 bytes foating point vaue, and each pixe in the cutter scene fies is stored as binary unsigned short integer. We transform the magnitude image into og-detected image and compute mutiresoution detection statistic in waveet domain both on target chips and cutter scenes, where target mutiscae mode is buit using 64 images imaged at a different aspect ange (extracting pixes target chip) from confuser zsu-23-4 training set. Using a broad range of threshods, cutter scenes have been processed with prescreening agorithm to extract the goba FAR. Then, the target testing chips have been processed to estimate the same range of threshods. We obtain receiver operating characteristic (ROC) curves, which represent the number of detections potted against the number of fase aarms at various threshod setting. Fig. 4 shows ROC curves for different detectors over the pubic MSTAR database, where CFAR is referred to two-parameter CFAR detector in image domain; others are mutiresoution CFAR detectors in waveet domain with different waveet basis functions. It iustrates performance of c(s) using db4 waveet outperforms others ROI Mask Threshod is determined according to the required FAR. Compared mutiresoution CFAR vaue to the threshod, a binary image is formed, and ROI is extracted after morphoogica processing. In order to identify the waveet coefficients beonging to the ROI or to the background, ROI mask is generated. ROI mask wi be expanded at each eve of decomposition due to fiter expansion [15]; this can be seen from the foowing inverse waveet transform.

8 292 Yuan, Zhu, and Zhang Figure 4. ROC curves over pubic MSTAR database in og-detected domain. x z 2 2 P% z 1 ( ) T s d P( z) z x Figure 5. Poyphase representation of waveet transform Factoring db4 Waveet Transforms into Lifting Steps Factoring db4 waveet transforms into ifting steps can be summarized as foows: 1) Poyphase representing db4 synthesis fiters h and g, we get synthesis poyphase matrix P (z). Db4 waveet is an orthogona base. Inverse transform uses two synthesis fiters h and g. Under the perfect reconstruction condition here we have [16]: h(z) = h 0 +h 1 z 1 +h 2 z 2 +h 3 z 3 +h 4 z 4 +h 5 z 5 +h 6 z 6 +h 7 z 7 (8) g(z) = h 7 z 6 h 6 z 5 + h 5 z 4 h 4 z 3 + h 3 z 2 h 2 z 1 + h 1 h 0 z 1 (9) Poyphase representation of waveet transforms can be iustrated schematicay in Fig. 5. Poyphase representation of synthesis fiters is given by h(z) = h e (z 2 ) + z 1 h o (z 2 ) (10) g(z) = g e (z 2 ) + z 1 g o (z 2 ) (11) where h e contains the even coefficients, and h o contains the odd coefficients.

9 Progress In Eectromagnetics Research B, Vo. 20, Corresponding synthesis poyphase matrix P (z) is [ ] he (z) g P (z) = e (z) h o (z) g o (z) [ ] h0 +h = 2 z 1 +h 4 z 2 +h 6 z 3 h 7 z 3 +h 5 z 2 +h 3 z+h 1 h 1 +h 3 z 1 +h 5 z 2 +h 7 z 3 h 6 z 3 h 4 z 2 (12) h 2 z h 0 2) Using Eucidean agorithm for Laurent poynomia, factor P (z) into ifting steps. P (z) can be factored into ifting steps ( )( )( )( ) (βz P (z)= 1 +β ) (ηz 1 +η ) ( α 1 )( )( 1 (λ3 z 3 + λ 2 z 2 + λ 1 z + λ 0 ) 0 1 (γz 1 +γ ) )( ) k k 1 (13) where coefficients are shown in Tabe 2. 3) Obtain anaysis poyphase matrix P (z 1 ) T ifting steps. Poyphase matrix P (z) is para-unitary, and the anaysis poyphase matrix is factored as P (z 1 ) T = P (z 1 ) T ( )( )( )( ) k 1 = k 1 (λ 3 z 3 +λ 2 z 2 +λ 1 z 1 +λ 0 ) ( )( )( ) 1 α 1 0 (ηz+η ) 1 )( 1 (γz+γ ) (βz+β ) (14) Tabe 2. Coefficients in ifting steps. k λ 0 1 λ λ λ η γ γ β β α

10 294 Yuan, Zhu, and Zhang This corresponds to the foowing impementation for the forward transform: s (1) = x 2 αx 2+1 d (1) = βs (1) +1 + β s (1) + x 2+1 s (2) = s (1) γd (1) +1 γ d (1) d (2) = ηs (2) +1 + η s (2) + d (1) s (3) = s (2) + d (2) d (3) = s (3) + d (2) d (4) = λ 3 s (3) 3 λ 2s (3) 2 λ 1s (3) 1 + s(3) + d (3) s = k 1 1 s(3) d = k 1 d (4) (15) 4) Derive the inverse transform from the forward by running the scheme backward. The inverse transform foows from reversing the operation above: d (4) s (3) = k 1 1 d = k 1 s d (3) = d (4) + λ 3 s (3) 3 + λ 2s (3) 2 + λ 1s (3) 1 s(3) d (2) = s (3) + d (3) s (2) = s (3) d (2) d (1) = d (2) + ηs (2) +1 η s (2) s (1) = s (2) + γd (1) +1 + γ d (1) x 2+1 = d (1) + βs (1) +1 β s (1) x 2 = s (1) + αx 2+1 where s, d are smoothed vaues and detais. (16)

11 Progress In Eectromagnetics Research B, Vo. 20, Generic ROI Mask Substituting corresponding equations in (16) into x 2 and x 2+1, we have x 2 = s s s s s s +2 x s d d d d +3 (17) = s s s s s s s d d d d +3 (18) From the above expressions, we know that the coefficients necessary to reconstruct x(2) and x(2 + 1) are ow frequency subband coefficients L( 3), L( 2), L( 1), L(), L(+1), L(+2), L(+3) and high frequency subband coefficients H(), H( + 1), H( + 2), H( + 3), as shown in Fig. 6. To simpify ROI shape information and aeviate the computation compexity of ROI mask, we specify a rectanguar region associated with the designated ROI. Thus, the identified ROI is bounded by a rectange. For the rectange, ony two positions need be studied, namey the upper eft and ower right corners. A sampes in between the two corners are considered to beong to the mask. Using the usua L () H () x(2 ) Figure 6. The inverse db4 tansform:the coefficients necessary to reconstruct x(2) and x(2 + 1) are L( 3) to L( + 3) and H() to H( + 3), respectivey.

12 296 Yuan, Zhu, and Zhang Tabe 3. ROI mask support. Generic nsup 3 psup 3 hnsup 0 hpsup 3 Modified ensup 2 onsup 2 epsup 0 opsup 2 hensup 0 hepsup 0 honsup 0 hopsup 2 convention that the origin of the subband coordinates occurs at the top-eft corner of the subband, the ROI in the parent subband covered the coefficients between (x 0, y 0 ) and (x 1, y 1 ), then in the new subband it wi cover (x 0, y 0 ) and (x 1, y 1 ), where x 0 = foor(x 0 /2) nsup, x 1 = foor(x 1 /2) + psup, y 0 = foor(y 0 /2) nsup y 1 = foor(y 1 /2) + psup (19) The choice of nsup and psup wi depend on whether the (x i, y i ) (i = 0, 1) is in the ow or the high frequency subband. Let constants nsup (ow, negative support), psup (ow, positive support), hnsup (high, negative support) and hpsup (high, positive support) denote support under the four situations. They are shown in Tabe 3. We assume that the ROI mask of subband HL 1, LH 1 and HH 1 is the same as that of subband LL Modified ROI Mask Athough higher bit rate is given to ROI, coding performance of target area degrades dramaticay if we mask significant waveet coefficient as formua (19). The reason is that the significant coefficients expand too fast even throughout the whoe image. To mitigate the situation above, in anaysis expression (17), (18) we note that each item has different effect on the reconstruction. Since the absoute maximum of the weight coefficient is about 2.0 ( ), the absoute weighting coefficient vaue smaer than 0.1 can be negigibe compared to it. So we ignore the item whose absoute weighting coefficient is smaer than 0.1, and expression

13 Progress In Eectromagnetics Research B, Vo. 20, L () H () x(2 ) Figure 7. To approximatey reconstruct x(2), the coefficients necessary are L( 2) to L() and H(); To approximatey reconstruct x(2 + 1), the coefficients necessary are L( 2) to L( + 2) and H() to H( + 2). (17), (18) are reformuated: x 2 x s s s d (20) s s s s s d d d +2 (21) We modify ROI mask formua (19) according to approximate reconstruction expressions (20), (21). The choice of nsup, psup wi depend on not ony whether (x 0, y 0 ), (x 1, y 1 ) is in the ow or high frequency subband but aso whether the x i, y i (i = 0, 1) vaue is odd or even. Let constants ensup (ow, even, negative support) etc. denote support under the eight situations. As shown in Tabe 3, mask support engths are shrunk after modification. Now ony ow frequency subband coefficients from L( 2) to L() and high subband coefficients H() are needed to approximatey reconstruct x(2), but ow frequency subband coefficients from L( 2) to L( + 2) and high subband coefficients from H() to H( + 2) are needed to approximatey reconstruct x(2 + 1), as show in Fig Rate Aocation The overa bit rates in bits per pixe for the targets and for the background are either provided by the user or determined by some automated means. Given R 1 bpp for the target and R 2 bpp for

14 298 Yuan, Zhu, and Zhang background, rate aocation procedure can be used to aocate rate in rate-distortion optimization sense for target and background sequences separatey. For target sequences, the rate aocation procedure is summarized as foows: 1) Initia λ and λ h, set the bound of bisection research for λ. 2) Computation λ = λ +λ h 2. 3) Chosen rate aocation vector T = (t 1, t 2,..., t M ), so that min M (α i σi 2E i(t i ) + λα i t i ), where σi 2 is the variance of sequence i, i=1 and E i (t i ) denotes the rate-distortion performance at t i bits/sampe. M is the number of data sequences (that is 22), and α i is a weighting coefficient to account for variabiity in sequence ength. 4) Computation T = M α i t i. If T > R 1, set λ = λ; if T < R 1, i=1 set λ h = λ; jump to (2). Rate aocation for background sequences is simiar to the target sequence, ony minor modification needed: R 1 in step (4) must be modified as R RESULTS To iustrate the performance of target-aided image inteigent compression agorithm, we do experiments on HB from BMP2 training set and HB from T72 testing set in MSTAR data base. Fig. 8(a) and Fig. 9(a) show their og-detected images, both of origina magnitude images are 16 bpp. We use a mutiresoution CFAR threshod of 3.84 [17]. Fig. 8(b) and Fig. 9(b) show ROI extracted and their mask. Studies previousy have shown that SAR (a) (b) (c) (d) (e) Figure 8. (a) HB (b) ROI and its mask. (c) TAIC with modified ROI mask (0.135 bpp, 2 bpp for target area). (d) TAIC with generic ROI mask (0.137 bpp, 2 bpp for target area). (e) JPEG2000 agorithm (0.14 bpp).

15 Progress In Eectromagnetics Research B, Vo. 20, imagery can be compressed by 4 : 1 to 8 : 1 without significanty degrading ATR performance [18]. So we give a arger bit rate of target area from 8 bpp to 2 bpp at step 1 bpp, very ow bit rate ony of 0.01 bpp to background data [19] in order to achieve arge compression. We evauate compression system performance by SNR s of target area (pixes corresponding to rectanguar area), which is defined in (22). Performance resuts are incuded in Tabes 4 and 5 at different bit rates. For comparison, the performance resuts of JPEG2000 agorithm are aso incuded. As verified in two tabes, TAIC agorithm shows exceptiona target area coding efficiency, and modified ROI mask enhances the performance. M N g(i, j) 2 i=1 j=1 SNR = 10 og 10 (22) M N [g(i, j) h(i, j)] 2 i=1 j=1 where g(i, j) is pixe vaue at site (i, j) in og-magnitude image, h(i, j) is accordingy decoded vaue, M and N are the dimensions of the image. Figures 8(c), 9(c) show how TAIC agorithm using modified ROI mask performs, and Figs. 8(d), 9(d) show how the TAIC agorithm using generic ROI mask performs. Figures 8(e), 9(e) show the performance of JPEG2000 agorithm. A of them are working at compression ratio 100 (bit rate for target area is 2 bpp). From these figures, we know TAIC agorithm with modified ROI mask outperforms that with generic ROI mask, and either of them performs better than JPEG2k agorithm. Context information (shadow) is preserved, which is usefu for ATR [20]. The computationa compexity of proposed agorithm is mainy determined by the size of image and target. We decompose the image (a) (b) (c) (d) (e) Figure 9. (a) HB (b) ROI and its mask. (c) TAIC with modified ROI mask (0.153 bpp, 2 bpp for target area). (d) TAIC with generic ROI mask (0.159 bpp, 2 bpp for target area). (e) JPEG2000 agorithm (0.14 bpp).

16 300 Yuan, Zhu, and Zhang Tabe 4. SNR of target area comparing our TAIC agorithm and JPEG2000 compression agorithm for HB bpp TAIC Modified ROI Generic ROI JPEG (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) Tabe 5. SNR of target area comparing our TAIC agorithm and JPEG2000 compression agorithm for HB bpp TAIC Modified ROI Generic ROI JPEG (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) (db) up to 4 eves. Mutiresoution target detection agorithm in waveet domain is much simper than two-parameter CFAR detection agorithm in image domain. ROI mask generation is simpe too. The robust quantizer is ony used to encode target sequences and eads to a much ess compex impementation since targets ony occupy sma parts of SAR image. The number of admissibe quantizers for each sequence is aso a sma constant compared to the size of image. Furthermore, the number of iterations for bisection search of a convex curve is reativey sma. Therefore, the tota computationa compexity of our agorithm is O(MN) for an image of size M N. 4. CONCLUSION We have investigated a target-aided SAR image inteigent compression system with a mutiresoution target detection scheme to designate ROIs, where ROIs are encoded with robust FRTCQ, and the remains are encoded with FRTCQ. We have found that mutiresoution CFAR detector using db4 waveet is more reiabe than others. We create generic ROI mask according to the ifting steps of db4 waveet inverse

17 Progress In Eectromagnetics Research B, Vo. 20, transform. To ameiorate the situation that significance coefficients expand too fast, which resuts in that the coding performance of target area degrades dramaticay, we modified ROI mask with respect to the expression of the inverse waveet transform. As coding resuts iustrate, TAIC agorithm performs better than JPEG2k agorithm, and modified ROI mask enhances the target area coding efficiency. The image coder enabes the transmission of very-high-quaity compressed imagery over imited bandwidth noisy channes without need for entropy coding and its associated compexity and susceptibiity to channe errors. The robust quantizer is ony used to encode target sequences which eads to a much ess compex impementation since targets occupy sma parts of SAR image. REFERENCES 1. Chan, Y. K. and V. C. Koo, An introduction to Synthetic Aperture Radar (SAR), Progress In Eectromagnetics Research B, Vo. 2, 27 60, Li, N.-J., C.-F. Hu, L.-X. Zhang, and J.-D. Xu, Overview of RCS extrapoation techniques to aircraft targets, Progress In Eectromagnetics Research B, Vo. 9, , Zou, B., H. Cai, L. Zhang, and M. Lin, Mode of man-made target beneath foiage using poinsar, Progress In Eectromagnetics Research Letters, Vo. 2, 21 28, Xue, W. and X.-W. Sun, Target detection of vehice voume detecting radar based on wigner-hough transform, Journa of Eectromagnetic Waves and Appications, Vo. 21, No. 11, , Lee, J. H. and H. T. Kim, Radar target discrimination using transient response reconstruction, Journa of Eectromagnetic Waves and Appications, Vo. 19, No. 5, , Jung, J. H., H. T. Kim, and K. T. Kim, Comparisons of four feature extraction approaches based on Fisher s inear discriminant criterion in radar target recognition, Journa of Eectromagnetic Waves and Appications, Vo. 21, No. 2, , Seo, D.-K., K. T. Kim, I. S. Choi, and H. T. Kim, Wideange radar target recognition with subcass concept, Progress In Eectromagnetics Research, PIER 44, , Aan, C., M. Kristo, L. Austin, and L. Margaret, A compression of detected SAR imagery with JPEG2000, SPIE, Vo. 4115, , 2000.

18 302 Yuan, Zhu, and Zhang 9. Zhang, J., Y. Huang, and H. Tian, SAR image compression based on image decomposing and targets extracting, 1st Asian and Pacific Conference on Synthetic Aperture Radar Proceedings, , Venkatraman, M., H. Kwon, and N. M. Nasrabadi, Objectbased SAR image compression using vector quantization, IEEE Transactions on Aerospace and Eectronic Systems, Vo. 36, No. 4, , Bonneau, R. and G. Abouseman, Target-aided fixed-quaity-ofservice compression of SAR imagery for transmission over noisy wireess channes, ICASSP 02, Vo. 4, IV-3413 IV-3416, Tanabe, N. and N. Farvardin, Subband image coding using entropy coded quantization over noisy channes, IEEE Journa Seect. Areas Commun., Vo. 10, , Abouseman, G. P., Waveet-based hyperspectra image coding using robust fixed-rate treis-coded quantization, SPIE, Vo. 3372, 74 85, Birney, K. and T. Fishcher, On the modeing of DCT and subband image data for compression, IEEE Transactions on Image Processing, Vo. 4, No. 2, , Park, K.-H. and H. W. Park, Region-of-interest coding based on set partioning in hierarchica trees, IEEE Transaction on Circuits and Systems for Video Technoogy, Vo. 12, No. 2, , Daubechies, I. and W. Swedens, Factoring waveet transforms into ifting steps, The Journa of Fourier Anaysis and Appications, Vo. 4, No. 3, , Novak, L. and S. Owirka, Effects of poarization and resoution on SAR ATR, IEEE Transactions on Aerospace and Eectronic Systems, Vo. 33, No. 1, , Gorman, J. D., Assessing the impact of image compression on SAR automatic target detection and cueing, Radar Conference, , Kim, A. and H. Krim, Hierarchica stochastic modeing of SAR imagery for segmentation/compression, IEEE Transaction on Signa Processing, Vo. 47, No. 2, , Lombardom, P., M. Sciotti, and L. M. Kapan, SAR prescreening using both target and shadow information, IEEE Radar Conference, , Atanta, USA, 2001.