A HYBRID SEABED CLASSIFICATION METHOD USING AIRBORNE LASER BATHYMETRIC DATA

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1 358 Journa of Marne Scence and Technoogy, Vo. 25, No. 3, pp (207) DOI: 0.69/JMST A HYBRID SEABED CLASSIFICATION METHOD USING AIRBORNE LASER BATHYMETRIC DATA Yung-Da Sun and Shahn-Wern Shyue Key words: Hybrd method, bathymetrc LDAR data, K-means and Support Vector Machne (KSVM), gray co-occurrence matrces (GLCM). ABSTRACT In recent years, Arborne Bathymetrc Lght Detecton and Rangng (LDAR) has been apped ntensvey to map coasta depth as we as for seabed cassfcaton. In ths study, we proposed a hybrd K-means and Support Vector Machne (KSVM) agorthm based on depth-derved gray-eve co-occurrence matrces (GLCM) from bathymetrc LDAR. Frst, the cacuated GLCM data set was used to sort K-means nto varous custers. Second, tranng sampes were seected on merged custers before appyng SVM cassfcaton. Fnay, we evauated the proposed hybrd agorthm n overa accuracy and the Kappa ndex. Compared to pure SVM, the proposed hybrd KSVM mproved the overa accuracy by 24%, and the Kappa ndex by 0.3. The resuts showed that the proposed KSVM method provded promsng resuts, n terms of accuracy and vsua nspecton. The benefts of the proposed cassfcaton method apped unsupervsed cassfcaton of K-means as pror nformaton for unseen seabed sedment types. Ths method was usefu, partcuary when ony depth-derved nformaton was avaabe, or where the ntensty/waveform had poor dscrmnaton propertes. I. INTRODUCTION There s ncreasng nterest n seabed cassfcaton for varous appcatons, such as coasta pannng, geoogca studes, and marne habtat montorng. Tradtonay, the coecton of seafoor sedment sampes has nvoved a tme-consumng and ow-coverage method for seabed characterzaton, but t contnues to be the bass for verfcaton of the automatc seabed machne earnng cassfcaton method. Comparng the sma sampe voume of grabs and cores, reatve to the extensve seabed area that coud be samped acoustcay, ndcates that Paper submtted 09/08/6; revsed /8/6; accepted 2/30/6. Author for correspondence: Yung-Da Sun (e-ma: mrbg.g @gma.com). Department of Marne Envronment and Engneerng, Natona Sun Yat-sen Unversty, Kaohsung, Tawan, R.O.C. ground-truth technques are tme-consumng, poory repcated, and expensve (Brown et a., 2004; McGonge et a., 2009). The shpboard acoustc remote sensng technque, known as the snge-beam or mut-beam echo-sounder (MBES) system, s usefu for characterzng seafoor sedment (Hars et a., 202). The MBES system provdes compete coverage of hgh-resouton bathymetry and backscatter nformaton of seabed topography wth mted cost (Smons et a., 2009), whch can accuratey defne detaed topography and potenta seabed habtats (Wson et a., 2007, Zavaas et a., 204). However, due to the potentay hgh rsk n the near-shore regon, caused by heavy wave nteracton or dangerous bottom topography (e.g., reefs), the MBES system has severa mtatons when apped to coasta or nshore boat-based surveys (Ryan et a., 2007). Arborne bathymetrc Lght Detecton and Rangng (LDAR) s a recent deveopment n remote sensng, wth great potenta for provdng hgh resouton and accurate Dgta Surface Modes (DSMs) n shaow water (Irsh et a., 999). It s a practca, effcent, and ow-cost approach that overcomes defcences of the MBES system n shaow water surveys (Costa et a., 2009). Recenty, seabed type and habtat cassfcaton, usng vdeography, MBES, and arborne bathymetrc LDAR data, have attracted a consderabe amount of attenton. MBES backscatter or LDAR ntensty has been modeed to be compared to expermenta data. Aternatvey, secondary features were extracted from MBES backscatter and LDAR ntensty usng statstca or texture anayses. Unt now, however, bathymetrc LDAR data have ony been processed to generate sea depth nformaton and seabed topography. However, we are st nterested n processng data from ths system that aows nformaton extracton wth actua seabed propertes. Some studes have compared bathymetry and backscatter data tranng sampes, n terms of densty dstrbuton and transect profes over varous bottom features (Costa et a., 2009; Zavaas et a., 204). These studes motvated us to use depth-derved features as nput for the cassfcaton method. Seabed or habtat cassfcaton s a compex, mut-source probem. Machne earnng methods (e.g., Support Vector Machne [SVM], K-means agorthm, neuro-fuzzy cassfers) have been apped to the extracted features by performng seafoor or habtat cassfcaton, and provde sgnfcanty mproved cassfcaton accuracy (Hasan et a., 202; Tyner et a., 204). The methods noted above mght be usefu when data types dffer n

2 Y.-D. Sun and S.-W. Shyue: A Hybrd Seabed Cassfcaton Way Usng ALB Data 359 Bathymetrc Ldar Ldar Data acquston Preprocessng GeoBock Bock Index Inverse Dstance of Weghtng, IDW a b c GLCM Texture Anayss ndem (a) Orgna (b) Contrast (c) Dssmarty KSVM Supervse [K-Means] Unsupervse [K-Means] Resut Tranng Sampe (ROI) Accuracy Assessment Test Sampe (ROI) Resut Fg.. Fowchart of proposed hybrd seabed cassfcaton method. ther statstca dstrbuton n one stacked dataset. Among them, the SVM method, a non-parametrc cassfer based on statstca earnng theory, s sutabe for cassfyng remote sensng, hgh dmensona, sma sampe sze data (Lodha et a., 2006; Waske and Benedktsson, 2007). In prevous studes, De Ameda et a., (2000) proposed an agorthm to speed up SVM earnng wth a pror custer seecton usng the K-means method wth smuaton data. Another study proposed a hybrd K-means and SVM method to extract features from cardotocography records to perform feta state cassfcaton (Chamdah and Wasto, 205). As wth the present study, the obectve of the cassfcaton agorthm n these two studes was to enhance the performance of the SVM cassfcaton. In ths study, we proposed a hybrd agorthm comprsng a two-step cassfcaton method utzng K-means and SVM (KSVM) for seabed sedment cassfcaton that appes to depth-derved features from bathymetrc LDAR topography data. Frst, we apped unsupervsed K-means cassfcaton to the gray-eve co-occurrence matrx (GLCM) features cacuated from bathymetrc LDAR topography data. Then, we seected tranng data sampes based on the K-mean resuts to avod ambguty. Fnay, we performed SVM cassfcaton to the GLCM features, and evauated the feasbty of the proposed hybrd agorthm n overa accuracy and Kappa ndex. The detas pertanng to the methodoogy w be ntroduced n the foowng secton. The expermenta resuts are presented and compared to the MLC and SVM approaches n Secton IV. II. METHODS Most prevous cassfers were based on snge cassfcaton methods, even when handng dfferent types of data. Athough these cassfers coud address the mtatons of tradtona parametrc agorthms, resutng n greater accuracy, these technques have drawbacks, ncudng hgh computatona cost and tme consumpton, to obtan optma cassfcaton parameters. Many prevous studes have ndcated that, f we coud remove or separate ambguous data from nput sources for a cassfer, d e f (d) Entropy (e) Homogenety (f) Second Moment Fg. 2. Ldar bathymetry for texture feature anayss dervatves t woud be much easer to sove the dffcutes n cassfcaton appcatons. Fg. shows the workfow of the proposed method, comprsng a hybrd agorthm for two-step seabed cassfcaton. Intay, the bathymetrc LDAR data were nterpoated to generate the topography DEM. Then, we used the DEM mage to perform texture anayss to cacuate the GLCM. We seected sx types of gray refectance co-occurrence matrx texture statstcs for testng, ncudng statstca vaues of homogenety, contrast, dssmarty, entropy and second moment, and correaton. We used a hybrd cassfcaton method, ncudng unsupervsed K-means and supervsed SVM agorthms. Fnay, the cassfcaton output was stored n ENVI natve mage format, and the cassfcaton accuracy was assessed n the overa accuracy and the Kappa ndex. A maor advantage of the proposed hybrd cassfcaton method s ts smpcty. Ths method coud be used wth common commerca remote-sensng software toos, such as ERDAS Image, Exes ENVI, or PCI Geomatcs, wthout programmng (Pathak and Dksht, 200).. Feature Extracton Intay, we took the bathymetrc LDAR data, wth a geographc management functon, to process a arge depth dataset. Then we apped the Inverse Dstance of Weghtng (IDW) method to generate the dgta eevaton mode (DEM). We used grayscae DEM mages, whch were composed of depth data, to do texture anayss. In ths step, a of the texture anayss technques were based on GLCM as proposed n Soh et a. (999). A matrx represents the number of occurrences of the reatonshp between pxe vaues and neghborng processng wndows, wthn a specfed dstance and drecton (Con et a., 20). We seected sx textura features n ths study, ncudng statstca vaues of homogenety, contrast, dssmarty, entropy and anguar second moment, and correaton. Observng the mage recognton resuts coud provde rch cassfed nformaton as

3 360 Journa of Marne Scence and Technoogy, Vo. 25, No. 3 (207) a reference. Fg. 2 presents the resuts from texture anayss. The foowng equatons defne these features. Let p(, ) be the (, )th entry n a normazed GLCM. The textura features can be cacuated from the foowng equatons (Harack and Shanmugam, 973): () Homogenety (2) Contrast (3) Dssmarty (4) Entropy f p (, ) 2 () Ng Ng N g 2 f2 n p(, ) n n0 (5) Anguar second moment (6) Correaton (2) f3 p(, ) (3) f4 p (, )og p (, ) (4) f 6 p 2 f5 (, ) (5) (, ) p(, ) x y where x and y, and x, and y are the means and standard devatons, respectvey, of p x and py. p(, ): (, )th entry n a normazed gray-tone spata dependence matrx, = P(, )/R. 2. KSVM hybrd Cassfcaton Method In remote sensng cassfcaton appcatons, the rues are usuay based on the tranng datasets, whch are acqured based on vsua nspecton from remote sensng magery. After cassfcaton, separate vadaton datasets are used to evauate cassfcaton accuracy. However, t s very straghtforward to seect tranng and test data from remote sensng magery. In the seafoor cassfcaton appcaton, the ony tranng and test datasets are ground truth. Because sampng the seafoor ground truth s tme-consumng and costy, the numbers of feasbe tranng and test datasets are mted. To overcome ths mtaton, we often seect tranng and test x y (6) datasets usng regon of nterest (ROI) on the stacked depthderved raster features to ncrease the number of tranng and test pxes. However, seectng and dentfyng accurate seabed sedment types to compe tranng and testng datasets for cassfcaton s not as straghtforward as usng remote sensng magery for and-cover appcatons. In addton, the poston accuracy for ground-truth sampng s sometmes mted by the equpment used or nfuenced by the sea status. Furthermore, most seafoor sedment s compex and mght vary over tme, whch s nfuenced by wave or ocean current transportaton effects n the nearshore regon. To overcome the above-mentoned mtatons, we apped the K-means unsupervsed cassfcaton n advance. The K-means agorthm was partcuary sutabe for custerng arge amounts of data. K-means custerng s a rapd and smpe method to partton feature space. It can be used to dvde the ndvdua measurements of bathymetrc depth data nto severa mutuay excusve custers. The K-means custer anayss nvoves an teratve aternatng fttng process, and the optma spt-eve s determned by the number of casses resutng from the ground truth. Because the K-means agorthm s an unsupervsed cassfcaton method, t s necessary to determne f the K-means derved custers exst for more than one abe n a sma regon. If ths s the case, further treatment s necessary to emnate sma patches. In ths study, we cassfed categores of seabed sedment types wth 0 teratons, as shown n Fg. 3(a). Then, we apped the maorty fter to merge the smaer cassfcaton resuts nto arger patches, as shown n Fg. 3(b). Fnay, we manuay combned smar categores nto four maor categores based on fed sedment sampes. Fg. 3(c) shows the resutng output, whch was used as the background magery to seect the ROI poygon as tranng sampes usng remote sensng software. Whe acqurng tranng sampes, we shoud keep the sze of the ROI poygon as sma as possbe. Ths coud avod the seecton of ROI poygons that mght ncude a varety of sedment characterstcs, as shown n Fg. 4. Athough we used the tranng sampe based on K-means cassfcaton resuts, ambguty between types st exsted. They coud not be resoved by tradtona parametrc cassfcaton methods, such as Maxmum Lkehood Cassfcaton (MLC). To address ths probem, we proposed SVM as the second cassfer of KSVM. The SVM s a powerfu mutvarate machne earnng agorthm based on statstca earnng theory (Vapnk et a., 995). It s bascay a bnary cassfer that maxmzes the margn between the tranng patterns and the decson boundary. The man task of the SVM tranng s to fnd an Optma Hyperpane Agorthm that can separate the two cass abes, represented as (-) and (), and f they exst, the vector w and scaar b were shown n Eq. (7): w x b f y w x b f y (7)

4 Y.-D. Sun and S.-W. Shyue: A Hybrd Seabed Cassfcaton Way Usng ALB Data 36 (a) (b) (c) Fg. 3. (a) Resuts from the K-means cassfcaton. (b) Resuts from appyng the maorty fter to the resuts from the K-means cassfcaton. (c) Combned resuts from the K-means cassfcaton. mn xyk x, 2 y y 0 St. 0 C,,2,, (9) where K(x, y ) = (x ), (y) s a kerne functon used to proect the data from nput space nto feature space. Our study cassfyng n the test area mpemented an agorthm usng SVM after K-means cassfcaton to separate four types of seabed matera and obtan a cassfcaton for the seabed. III. DATA Fg. 4. The seected ROI. Gven a tranng set of nstance-abe pars (x, y ), =,, where x R n and y {, -}, the SVM requres the formuaton of the foowng optmzaton probem. T mn wwc 2 T y w ( x) b St. 0,, 2,, where w s an n-dmensona vector perpendcuar to the hyperpane, and C s the penaty parameter that contros the edge baance of the error. Usng the technque of Lagrange mutpers, the optmzaton probem becomes: (8). Study Area and Datasets The study ste, Huaen Harbor, s an nternatona harbor ocated n the eastern coast of Tawan (Fg. 5). It s a narrow and ong artfca harbor eanng towards the Tawan Centra Mountans to the west. The domnant seafoor sedment types n the Huaen Harbor coasta area are mosty sand and grave, due to streams mportng offshore turbd water. Near shore regons have severa reefs and rock sedment types. LDAR data were acqured n 2008, usng the Optech bathymetrc LDAR, Scannng Hydrographc Operatona Arborne LDAR Survey (SHOALS) 000T system. Ths system utzed remotey coected topographc and bathymetrc measurements, usng nfrared (064 nm) and bue-green (532 nm) scannng aser puses wth a vertca accuracy of 20 cm and a horzonta accuracy of.5 m. The fght heght of ths experment was 300 m to 400 m, usng the fxed-wng arcraft BN-2B wth 22 routes for the regon near the Huaan Harbor. The maxmum depth of ths area was about 28 m. Two sets of scan parameters were used n ths

5 362 Journa of Marne Scence and Technoogy, Vo. 25, No. 3 (207) 9 E 20 E 2 E 22 E 23 E 24 E Tabe. The tranng data and tests used for study area. Categores Tranng Sampes Test Sampes 26 N CHINA 25 N 24 N TAIWAN 26 N 25 N 24 N Sand Mud Mud-Sand Rock N 23 N 22 N 2 N HUALAIN 22 N 2 N 20 N 20 N 9 N 9 N Sand Ms Mud Rock Sand Ms Mud Rock 8 N 8 N (a) Maxmum kehood cassfer (b) Support vector machne Fg. 6. Seabed cassfcaton comparson map. 9 E 20 E 2 E 22 E Fg. 5. Study area. 23 E 24 E survey area. One set was 300 m fyng heght and a pont coud densty of 3 3 m, and another was 400 m fyng heght wth 5 5 m pont coud densty. The tota amount of bathymetrc LDAR data from the Huaen Harbor was,262,383 data ponts, wth a grd densty of 500 m 500 m. In the past, mut-beam echo sounders and sde scan sonar were based on the ntensty of the sonar transmtter to trace the refectance for extractng sedment characterstc nformaton. Hewtt et a. (200) used mut-beam echo sounders nvestgaton whch was based on the backscatter to characterze seafoor features. Hamton et a. (20) aso research acoustc seabed segmentaton from drect statstca custerng of entre mut-beam sonar backscatter curves. We have cted them n ths secton. 2. Ground-Truth Data Researchers dscovered an estuary of the Hua-en, Ge-An, and Me-Lun Rvers Large streams (e.g., Huaen Rver) woud dscharge mud and suspended sedment to the sea. Most coasta sedments were composed of mxed st. In the past, most softsedment ground truth reed on the use of tradtona sedmentsampng gear, such as grabs and corers. Durng 2003 and 2004, we coected sedment sampes wth grabs durng a snge beam hydrographc survey. The seabed sedment conssted of dfferent szes, shapes, and specfc gravtes. To assgn ground-truth casses to seabed cassfed data, the hybrd method was apped by searchng for the nearest maorty cass wthn the feature of the reatve ocaton. The spata poston of the dfferent categores was chosen by dfferent depths and aong survey nes. About 70% of a avaabe reference data were randomy samped for mode deveop- ment, and 30% for fna verfcaton and accuracy assessment. In our experments, tota tranng sampes from four seabed categores (sand, mud, mud-sand, and rock) were seected from the bathymetrc LDAR depth data (952 pxes), and 267 pxes for test sampes (Tabe ). IV. RESULTS AND DISCUSSION. Hybrd Cassfcaton Experments Tradtona statstca MLC and SVM cassfcatons were performed as two standard cases for comparson to nvestgate the accuracy of our proposed hybrd seabed cassfcaton method. The feature vectors used for ths experment were L- DAR depth-derved GLCM features, ncudng homogenety, contrast, dssmarty, entropy and anguar second moment, and correaton. Because the SVM non-parametrc cassfers requred numerous parameters, the SVM cassfer wth the set of parameters resutng n the hghest accuracy s reported here. To effectvey dentfy parameters for SVM, we adopted bsvm and mage SVM (Verng et a., 2008; Chh-Chung Chang et a., 20) toos to obtan optma penaty parameters and the gamma vaue of the rada bass kerne functon. Next, the hybrd cassfcaton, based on unsupervsed K-means and SVM (case KSVM), was used to compare the performance and accuracy of seabed cassfcaton. Tabe 2 sts accuracy assessment resuts for the three experments. The test area was cassfed correcty, assumng that cassfcaton derved from the ground truth by hydrographc grab was accurate, and estmated from the confuson matrx. Overa accuraces of the standard MLC and SVM experments were 57.39% and 6.42%, respectvey. Fgs. 6(a) and (b) present the resuts from MLC and SVM, respectvey, whch u-

6 Y.-D. Sun and S.-W. Shyue: A Hybrd Seabed Cassfcaton Way Usng ALB Data 363 Experment OA (%) KI Tabe 2. Comparatve resuts usng MLC, SVM, and hybrd (KSVM) agorthms. Producer Accuracy (%) User Accuracy (%) S MS Mud R S MS Mud R MLC SVM KSVM OA: Overa accuracy (%) KI: Kappa ndex S: Sand Notes: KSVM means, K-mean,s and SVM. MS: Mud-Sand M: Mud R: Rock superor. User accuracy ndcates the probabty that the actua map pxe represents the category on the seabed, whe producer accuracy s the probabty of a reference pxe beng correcty cassfed (Jensen, 2005). Sand Ms Mud Rock Fg. 7. Cassfcaton resuts from the hybrd method. strate the cassfcaton resuts for standard cases, usng the pure MLC and SVM methods. From Tabe 2, we can see that the Kappa statstc vaue obtaned n the anayss for MLC was , whch was ower than the overa accuracy (0.5739). Dfferences between these two vaues were to be expected, as each ncorporated nformaton from the confuson matrx. Overa accuracy ony ncuded data aong the maor dagonas and excuded the errors of omsson and commsson, whereas the K-statstc ncorporated the non-dagona eements of the confuson matrx. The same stuaton coud be observed n the SVM case. The overa accuracy of the standard KSVM experment was 85.39%, and the Kappa ndex for the KSVM experment was Fg. 7 shows the cassfcaton resuts from the hybrd method, whch ndcate that the proposed hybrd cassfcaton method KSVM s superor to pure MLC and SVM. The producer and user accuracy were cacuated to nvestgate the ndvdua cass accuracy. Accordng to Tabe 2, athough the producer accuracy of the SVM (98.59%) was better than the KSVM (87.32%), n genera, the KSVM experment was 2. Dscusson Our test area was about 0 km 2, whch was seected to represent the compexty n bathymetry data over the Huaen Port. The hybrd method was used to cassfy the depth varabes derved from bathymetrc LDAR data to dstngush seabed habtats. Generay, numerous cassfers are capabe of usng LDAR backscatter ntensty or waveform data to cassfy dfferent habtats. Some cassfers provde hgher accuracy. The appcaton of automated cassfers usng backscatter data has become more common, but has sedom nvoved bathymetry data, due to the reatvey sma amount of nformaton that coud be extracted. For a cassfcaton method based on texture anayss features, t was mportant to confrm that the sze of ROI of tranng pxes was arge enough wth respect to the texture varaton, to ensure that the tranng sampes were nvarant wthn feature types. In contrast, to construct a cassfer, t was expected to be sma enough to ensure that each tranng ROI dd not contan more than one feature type. Therefore, t was very dffcut to seect an optma ROI sze, wth respect to varous GLCM processng wndow szes, for a cassfcaton system based on GLCM features. Therefore, the SVMs machne, based on a pror custer seecton derved from the K-means unsupervsed method, coud provde a feasbe method to seect proper ROI sze over varous GLCM wndow szes. Accuracy ncreased wth ncreasng GLCM processng wndow sze, whch coud be observed from the overa accuracy and Kappa coeffcent (Tabe 3). We cacuated the GLCM features for the KSVM experment wth 7 7, 9 9, and processng wndows. Comparng the accuracy of these datasets, we assessed the confuson matrx. The resuts of the comparson showed that the wndow was the optmum processng wndow, as defned n Eqs. () through (6). The overa accuracy and Kappa coeffcent both ndcated that the cassfcaton resut was proportona to the processng wndow.

7 364 Journa of Marne Scence and Technoogy, Vo. 25, No. 3 (207) Tabe 3. Comparson of accuracy of dfferent wndows wth hybrd cassfcaton. Wndows Seabed Overa Accuracy (%) Kappa Coeffcent V. CONCLUSIONS In ths study, we proposed a new approach to sutabe seabed cassfcaton, based on the bathymetrc LDAR depth-derved features as nput for cassfcaton. It was usefu, partcuary when ony depth-derved nformaton was avaabe or where ntensty/waveform mght have poor dscrmnaton propertes. Ths study showed that the seabed cassfcaton based on LDAR depth-derved features provded promsng resuts, n terms of accuracy and vsua nspecton. The proposed cassfcaton method appes K-means unsupervsed cassfcaton as a pror knowedge for unseen seabed sedment types. It provdes a feasbe way to seect proper ROI over varous GLCM wndows szes, for a cassfcaton system based on GLCM features. The proposed hybrd cassfcaton method shows how unsupervsed K-means cassfcaton resoves the dffcuty, whe appyng pure SVM to GLCM-based feature cassfcaton appcatons (ths can be seen by comparng Fgs. 6 and 7). In ths study, we proposed a hybrd method to cassfy seabed sedment types for substratum maps, n partcuar those ackng adequate ground truthng. The method used the fact that K-means can be used to estmate the number of custer centers reated to dfferent unknown seabed types and subsequenty samped at severa obvous stes to verfy ther physca characterstcs. REFERENCES Brown, C. J., A. J. Hewer, W. J. Meadows, D. S. Lmpenny, K. M. Cooper and H. L. Rees (2004). Mappng seabed botopes at hastngs shnge bank, eastern Engsh Channe. Part. Assessment usng sdescan sonar. Journa of the Marne Boogca Assocaton of the UK 84(03), Chamdah, N. and I. Wasto (205). 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