HIGH PERFORMANCE PRE-SEGMENTATION ALGORITHM FOR SONAR IMAGES

Transcription

1 HIGH PERFORMANCE PRE-SEGMENTATION ALGORITHM FOR SONAR IMAGES Benjain Lehann*, Konstantinos Siantidis*, Dieter Kraus** *ATLAS ELEKTRONIK GbH Sebaldsbrücker Heerstraße 235 D Breen, GERMANY Eail: **Hochschule Breen, University of Applied Sciences Institute of Water-Acoustics, Sonar-Engineering and Signal-Theory (IWSS) Neustadtswall Breen, GERMANY Eail: Abstract: This paper proposes a fast pre-segentation algorith based on atched filtering eploying integral iages. It is suitable for identifying regions of interest (ROI) in sonar iages, in particular the detection of anoalies on the sea floor with focus on proud ground ines. Mines placed on the sea floor are still a vast threat in civil and ilitary shipping. This potential risk is typically encountered by advanced sonar signal processing techniques and tie consuing anual evaluation of the sonar data by a huan operator. Due to ission specific tie constraints a coputer aided or even autonoous analysis of the huge aount of data is desired. A fast pre-segentation of regions of interest (ROI) reduces not only the aount of data for the huan operator and consecutive classification algoriths significantly; it also allows for a real tie processing syste applicable in autonoous underwater vehicles (AUV). Keywords: Sonar detection, synthetic aperture sonar, iage segentation, object segentation, real tie systes

2 1. INTRODUCTION Advanced sonar signal processing techniques as e.g. synthetic aperture sonar (SAS) processing offer sea floor apping with high, alost optical-like quality. In conjunction with autonoous underwater vehicles (AUV) such sonar systes collect data that result in iages with constant resolution of a few centietres together with a swath width up to hundred etres and ore. After a several hours lasting ission the vast aount of recorded data represents a tie consuing challenge for a huan operator especially in ters of object recognition. To assist, or even replace the huan operator copletely, iage processing techniques are applied. Due to the huge aount of recorded data the direct application of sophisticated segentation algoriths is not feasible. Therefore a pre-segentation step that extracts ROI becoes andatory. Hence, we propose a atched filtering approach based on integral iages to cope with the high requireents concerning real-tie processing and reliability. This paper shows in section 2 the potentials and liitations of atched filtering for presegentation, in section 3 the integral iage approach is introduced, section 4 shows how integral iages are used for atched filtering, section 5 gives soe experiental results showing the perforance gain obtained and finally section concludes this paper. 2. MATCHED FILTERING FOR ROI DETECTION In sonar iagery a target on the sea floor eerges with two characteristics. The first one is a proinent highlight which is actually the target itself, caused by a high backscattering of the transitted acoustic wave field. The second one is the acoustic shadow caused by shielding the field and therefore diing out the backscattering of the area located closely behind the target, cf. [1]. These two effects depend on different circustances like the flight height (or height over ground) h of the sonar syste, distance d to the target, target size, etc. In Figure 1 the general setup is suarized. f Fig. 1: Usual underwater scenario with a target on the seafloor scanned by an AUV and the foration of a highlight shadow structure expressed with the noralized backscatter strength.

3 Figure 2(a) shows an exaple for a ROI of a SAS iage containing a cylindrical target. To find such a ROI in a typical SAS iage a atched filter can be applied. (a) (b) (c) (d) (e) (f) Fig. 2 : (a) shows a ROI (81 10 pixels, ) within a SAS iage u(x,y) containing a cylinder, (b) exposes a filter ask (x,y) with the corresponding values +1 for highlight (bright grey), 0 for background (white) and -1 for shadow (dark grey), (c) the correlation result v(x,y), in the range [0,1], (d) shows a ROI (81 12 pixels, ) within a SAS iage u(x,y) containing a cylinder, (e) exposes also a atched filter ask (x,y) and (f) shows the correlation result v(x,y) of (d) with (e). One can observe in Figure 2(a) that the two regions, highlight and shadow, differ strongly in their pixel intensities (iage range [0, 255], where 0 is blue and 255 is red). While the highlight pixel values are typically in the upper third of the intensity-level scale, the shadow is always located in the lower third. We also notice that there is a sall stripe of background pixels located between highlight and shadow. This characteristic can be used to create a filter ask ( x, y ), {0,., N 1} {0,., M 1}, that ephasizes regions with this particular topography, Figure 2(b) shows an exaple for a filter ask. Before applying the ask ( x, y ) to the sonar iage u( x, y ) a pre-processing is necessary, with {0,., N 1} {0,., M 1}. After setting the ean value of the iage to zero N 1 M 1 1 uɶ = u u( i, j), (1) NM i= 0 j= 0 the pixel values in shadow regions are predoinantly negative, which leads to the intended aplification of the filter action for the proposed ask. To weight the highlight and shadow regions with the sae iportance, the next processing step constitutes a noralization to + 1 and 1 respectively, by eans of uɶ, if uɶ < 0 in( uɶ uɶ u =, if uɶ > 0. ax( uɶ 0, elsewhere (2)

4 After scaling the iage range the correlation of u( x, y ) with ( x, y ) is perfored yielding the correlation result N 1 M 1 1 v = ( k, l) u( x + k, y + l) N M 1 = N M k = 0 l= 0 ( x y x y ) 1 * F M ( f, f ) U ( f, f ), (3) where targets shaped siilar to the filter ask ( x, y ) are ephasized. U ( f, f ) is x the Fourier transfor of u( x, y ) and M ( f, f ) the Fourier transfor of the ask, respectively. An exaple of a atched filter ask is exposed in Figure 2(b) where the filter ask paraeters ( M, N, s, h ) have been selected in accordance to the expected target diensions, cf. Figure 2(a). The correlation result is shown in Figure 2(c) with a axiu value of 0.703, which indicates the correlation between ask and target. Another exaple for a ROI and a corresponding ask is depicted in Figure 2(d)-(e). The cylindrical target in Figure 2(d) is not parallel orientated to the sonar syste which appears in the recorded iage as an affine transforation. Using the ask given in Figure 2(b) for Figure 2(d) yields a correlation value of only A ore appropriate filter ask is shown in Figure 2(e) and the correlation result in 2(f) with its axiu correlation of Hence, the application of only one filter ask is not satisfactory. Therefore rather a certain bank of filter asks is necessary to cope with targets differing in shape, size and orientation. To assure the real tie feasibility of such a atched filter bank approach the filtering process has to be accelerated. For this reason section 3 addresses the integral iage representations to provide the basis for such an acceleration. y x y 3. INTEGRAL AND ROTATED INTEGRAL IMAGES Two-diensional iage features can be rapidly coputed using an interediate representation known as the integral iage by Viola et al. [2] or sued area tables by Crow [3]. The integral iage denoted as II contains at location ( x, y ) the su of all pixel values above and to the left of a given input iage u( x, y ) = II u( x, y ). x x, y y (4) The integral iage II( x, y ), given the iage u( x, y ), can be calculated with one pass over the iage doain ranging fro the top left corner at (0,0) to the botto right ( N 1, M 1) by eans of II = II( x, y 1) + II( x 1, y) + u II( x 1, y 1), (5)

5 with II( x, 1) = II( 1, y) 0. Having the integral iage in ind, the su of pixel values within a rectangular region of u aligned with the coordinate axis can be coputed with four array references. To copute the su over all pixel values contained in area A of the size a b, a b storage accesses and ab 1additions are necessary, while with II( x, y ) only 4 storage accesses and 3 additions are needed, following the equivalence I = u( i, j) = L + L ( L + L ), A i, j A with L = II( x 1, y 1), L = II( x + a, y 1), L = II( x 1, y + b), L = II( x + a, y + b) () Depending on the application, this technique can reduce the coputational burden treendously. In addition to areas aligned with the coordinate axis, we pursue the idea of Rotated Sued Area Table by Lienhart et al. in [4], where rectangular areas rotated by 45 are considered. In the style of our notation we call this second interediate representation Rotated Integral Iage, RII( x, y ). It can be calculated with two passes over all pixels. The first pass fro left to right and top to botto deterines RII = RII( x 1, y 1) + RII( x 1, y + 1) + u RII( x 2, y 1), (7) with RII( 1, y) = RII( 2, y) = RII( x, 1) 0. Followed by the second pass fro right to left and botto to top RII = RII + RII( x 1, y + 1) RII ( x 2, y). (8) The calculation of the su of pixel values in a rotated rectangular area is also easily done by referencing again four positions in RII( x, y ) I = u( i, j) = L + L ( L + L ). Ar r 4 r1 r 2 r3 ( i, j) Ar (9) For a detailed explanation we refer to [4]. The benefit of these ethods is the constant tie that is needed to calculate the su of pixel values within an area independently of its size, since only four pixel references and three additions are necessary - if the interediate representations are once calculated. 4. MATCHED FILTERING WITH INTEGRAL IMAGES In the following we replace the correlation, cf. (3), in the iage doain by a calculation in the integral iage doain. In the iage doain a filter ask is supposed to slide over the whole doain, addressing with the centre pixel (reference pixel) all iage pixels, as shown in Figure 3(a). Using the integral iage, filtering equals addressing the eight reference points properly and sliding these points over the whole integral iage doain,

6 indicated in Figure 3(b). This consideration is for one particular filter ask, changing the filter ask corresponds to a change in array references in the integral iage doain. (a) II( x, y ) (b) RII( x, y ) Fig. 3: Shows an area calculation for I A using the integral iage (a) and (b) using the rotated integral iage. The reference points for the areas are the very first pixel for each area respectively, where the origin is the upper left corner. To calculate the corresponding atched filter results we follow 1 v = ( Lh 1 + Lh 4 ( Lh 2 + Lh 3) [ Ls 1 + Ls 4 ( Ls 2 + Ls 3) ] ), (10) N M where Lhj and L sj ( j = {1, 2,3, 4} ) denote the four references for the highlight and shadow ask respectively. 5. EXPERIMENTS Section 4 shows two representations that replace the correlation in tie or the ultiplication in the frequency doain by exploiting the integral iage approach. Considering the application of a filter bank this ethod can decrease the coputational burden significantly. In order to use the II and the RII( x, y ) we ake in the first step an assessent whether the use of filter asks aligned with the coordinate axis or its 45 rotated version are reasonable. The second step yields experiental results for real data. A. Filterasks Considering the distorted version of a cylindrical target in Figure 2(d) the question arises if the interediate representations with their liited angular range are sufficient to be applied for those targets. Therefore different filter asks were created in order to find the axiu correlation between 2(d) and those asks. The filter asks are shown in Figure 4 (first row) sorted by their increasing coputational coplexity with the following properties:

7 (, ) 1 u v : ask orientations {0,90 } (, ) 2 u v : ask orientations {45,135 } (, ) 3 u v : highlight ask orientations {45,135 } shadow ask orientations {0,90 } (, ) 4 u v : ask orientation [0,180 ] (, ) 5 u v : arbitrary shaped and orientated asks The second and third row of Figure 4 show vi ( x, y ) ( i = {1, 2,3,4,5} ) for Figure 2(d) and Figure 2(a) respectively. The intuitive expectation that (, ) 5 u v is the best choice for Figure 2(d) is approved, as indicated by the axiu of vi underneath the iage. Coparing ( u, v ) with ( u, v ), we notice that 3 5 (, ) 3 u v yields alost the sae axiu value as ( u, v ). 5 ( u, v ) ( u, v ) ( u, v ) ( u, v ) ( u, v ) Fig. 4: First row shows the filter asks in ascending order with their coputational load, second row shows the correlation result for Figure 2(d) with the corresponding correlation coefficient below and the third row shows the correlation result for Figure 2(a) with the corresponding correlation coefficient below.

8 To obtain a good balance between coputational efficiency and significant ROI indications for rotated targets we choose asks of the type (, ) 3 u v. This result was confired with additional experients. Fro now on we apply only filter asks of the type ( u, v ) 1 and (, ) 3 u v. With this approxiation the coputational burden for filtering is still calculating two interediate representations and calculate for each single filter operation the su over eight array references. B. Real Data After finding the balance between ask approxiation, coputational load and good ROI indication, we consider a real data application. The here used data set consists of sonar iages covering an area of 0.72 k including 213 targets of different nature (ain target types: cylinders, cones, wedged like targets). In this real data experients the following key figures are considered: Masks : nuber of used asks r t : detection rate in percentage R f : false alars per target O( cor) O( II ) : the ratio of needed basic operations using correlation to basic operations using integral iages O( FFT) O( II ) : the ratio of needed basic operations using the Fourier transfor to basic operations using integral iages. We assue for the efficiency of the Fourier transfor that the iages are always of the size 2 N. Ao A s : the ratio of the area of the original data set to the area of all detections after pre-segentation. It shows the reduction in area requiring a post-processing. Masks rt Rf O( cor) O( II ) ( ) ( ) A A O FFT O II o s 1 71 % % % % % % % Table 1: Results for the overall perforance using in the first row only one ask and in the seventh row seven asks. The grey results are not of interest because not all targets were found.

9 In Table 1 the results for the real data set are shown. Beginning with only one filter ask and issing 30 % of all targets, the detections rate increases with every added ask. Since the single filter asks are logical or related, with exclusion of ultiple found regions, the false detection rate increases also. The coputational load is severely reduced by using the integral iage approach, shown in Table 1 colun four to six considering 7 asks. C. Coputational Load We give in addition to our experients an assessent about the coputational load and soe rearks concerning the ipleentation. Figure 5 shows in (a) the nuber of basic operations for a fixed iage size using an increasing nuber of asks which is siilar to our setup. Figure 5(a) shows that the use of 20 filter asks with the integral iage approach costs less then 50% of the calculations for one filter ask in the frequency doain. Figure 5(b) shows the nuber of basic operations over an increasing iage size with a fixed aount of asks (seven asks). The intersection shows for how any iage pixel the integral iage approach perfors better than the Fourier transfor, naely for iages 4 bigger than 7 10 pixels. This size corresponds for the here used syste to an area of 50 2 and a recording tie of about 0.1 seconds. (a) (b) (c) Fig. 5: (a) Overview for the needed basic operations vs. the nuber of asks, (b) vs. iage size in pixels with seven asks and (c) shows an efficient ipleentation schee. In order to realize an efficient ipleentation for further real data applications, expecting ore or ore coplex target structures within, we propose a straight forward ipleentation. Equation (10) is split up into two parts, the highlight ask and the shadow ask ( x, y ). The general setup is shown in Figure 5(c), where II and RII are s used as the input. The highlight asks,., 1 and shadow asks,., 1 providing h the correlation results v,., h1 v hk and v,., s1 v sk respectively. The only difference between a highlight and a shadow ask is the sign, highlight asks have positive values while hk s h sk

10 j shadow asks have negative values. The next operator D j is a local shift operator and needed because all asks differ in their size and have therefore different reference points. To asseble a coplete filter ask result (cf. Figure 3) the partial filter ask results have to be added in conjunction with the reference points. After providing 2k correlation results, two partial filter results are addressed by two ultiplexers and added to obtain the 2 final correlation result v ( x, y ), with j {1,.,(2 k). With the use of two ultiplexer fol- 2 lowed by a ultiplication with ± 1, one can obtain up to (2 k) filter results by using only 2k partial filter asks. In addition to this, this processing is perfectly suited for parallel coputing.. CONCLUSION We introduced a fast pre-segentation ethod in order to reduce the coputational load. We take the coputational load for different kinds of asks into account and proposed for distorted targets a siplified ask which needs only the integral iage and the rotated integral iage. For the real data experients the algoriths outperfors the standard tool, the Fourier transfor, for this application considerably. Due to not severely increasing coputational coplexity, additional filter asks with an appropriate fusion strategy can decrease the false alar rate. This ethod is perfectly suited for the use on AUVs with SAS systes ounted. 7. ACKNOWLEDGEMENTS The authors would like to thank the people fro JRP on Detection & Classification fro NURC for support and providing us with SAS iages gathered by the MUSCLE syste, as well as ATLAS UK for providing high quality SAS data collected with the VISION 00 syste. REFERENCES [1] P. Blondel, The Handbook of Sidescan Sonar, Springer, [2] P. Viola, M. Jones, Robust real-tie face detection, JICV 2001, vol. 57, pp [3] F.C. Crow, Sued-area tables for texture apping, In Int. conf. on coputer graphics and interactive techniques, pp , [4] R. Lienhart, J. Maydt, An extended set of haar-like features for rapid object detection, In IEEE ICIP 2002, pp