AUTOMATED LOCATION OF ICE REGIONS IN RADARSAT SAR IMAGERY

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1 AUTOMATED LOCATION OF ICE REGIONS IN RADARSAT SAR IMAGERY Chistophe Waceman (1), William G. Pichel (2), Pablo Clement-Colón (2) (1) Geneal Dynamics Advanced Infomation Systems, P.O. Box Ann Abo Michigan USA (2) NOAA/NESDIS, WWB E/RA3, Room Auth Rd. Camp Spings MD USA ABSTRACT A supevised classification algoithm has been developed to automatically emove egions of ice fom consideation by ship detection and wind vecto estimation algoithms. The output fom the classifie is then put though a seies of ule-based modifications to eliminate eoneous classifications that do not have the coect spatial elationships. Pefomance analysis on RADARSAT ScanSAR Wide imagey shows a 7% mis-classification ate with the classification algoithm, all of which ae coected by the subsequent set of spatial ules. 1. INTRODUCTION As pat of the NOAA/NESDIS Alasa SAR Demonstation Poect [1], a multi-yea demonstation of the poduction and use of SAR imagey to geneate poducts in a pe-opeational envionment, a ship detection poduct is geneated that automatically locates lage ships within the SAR image [2] and a wind poduct is geneated that automatically geneates wind vectos ove the ocean [3]. It was found that egions of ice geneated huge numbes of false ship detections, which made images with ice egions unusable without a manual intepetation of the esults. In addition, the wind poduct elies on models fo the ada coss section of the wate which ae inappopiate fo ice egions, and thus geneate eoneous wind vectos ove ice. Fo both of these poducts, an automated way to locate ice egions and exclude them fom the pocessing of the algoithms could significantly impove the quality of the poducts. Theefoe a study was launched to develop an algoithm that could automatically locate ice egions in RADARSAT SAR imagey. 2. CLASSIFICATION ALGORITHM The algoithm appoach was based on pevious wo done fo ice classification in the maginal ice zone [4] and fo teain classification using dual-antenna aibone SAR systems [5]. It is a supevised classifie that fist gets tained on examples of the types of classes to be geneated in ode to ceate a seies of classification vectos, then applies these classification vectos to sepaate the image egions into classes. Assume that thee ae N classes desied, and the use has specified a window size, M 1 x M 2, that will be used to classify the image. The window must be big enough to contain the tone and textue infomation that can diffeentiate between the classes, yet small enough to geneate sufficient spatial esolution fo the esulting classification. The use has also specified a seies of algoithms that ae applied to the image samples within the M 1 x M 2 window and that geneate measues of the tonal o textue infomation within the window. That is, each algoithm inputs the image values within the window and outputs a scala quantity that measues, fo example, the statistics, textue, o coelation popeties of the image within the window. The scala outputs fom all of the algoithms ae assembled into a featue vecto, f, that will be used to classify the samples within the window. Fom the taining of the algoithm, we will geneate the mean featue vecto fo each class: m whee goes fom 1 to N. Also fom the taining we will geneate a classification vecto fo each pai of classes, c, and a scala theshold value, T, whee goes fom 1 to N and goes fom 1 to N. The classification pocess is then as follows. Fo each pai of classes, the scala p is fomed via p ( f m ) c = fo (1) whee epesents the vecto dot poduct. If p < T fo all = 1 to N,, then the image samples within the window ae assign to class. In essence, the algoithm assumes that in the space of the image featues epesented by the featue vecto f, each class occupies a unique convex egion. The classification vectos, c, define hypeplanes that sepaate pais of classes, whee the hypeplane is othogonal to the classification vecto and located at a distance T fom the location of the mean featue vecto fom class along the classification vecto. A featue vecto is assigned to class if it is on the coect side of each hypeplane between class and each othe class, which implies that p fom Eq. 1 is < T fo all = 1 to N,. Note that we can always nomalize the length of such that c

2 ( m m ) c 1 = (2) so that the theshold values, T, ae always between 0 and 1. The classification vectos ae geneated in the taining session by finding the vecto diection that maximizes the distance between the two classes in the featue space. Specifically, if we let s epesent the scala values geneated fom the dot poduct of the featue vectos fom class and the classification vecto c, and liewise let s epesent the dot poduct values between the class featue vectos and c, then we want to define c such that we maximize the distance metic, d, defined as 2 ( E[ s ] E[ s ]) ( va[ s ] + va[ s ]) d = (3) whee E[] and va[] epesent the mean and vaiance, espectively of the values within the bacets. Note that we can e-wite Eq. (3) as T cmc d = T c( C + C ) c (4) whee M is a matix defined as ( m m )( m m ) T M =, (5) C is the covaiance matix fo the featue vectos fom class and C is the covaiance matix fo the featue vectos fom class. We can e-wite Eq. (4) as an eigenvalue poblem 1 ( C + C ) Mc = dc (6) whee the -1 supescipt indicates matix invesion. The classification vecto we want is the eigenvecto that geneates the maximal eigenvalue and thus maximizes the distance metic d. Since the matix M has unit an, Eq. (6) has only a single eigenvecto solution which can be witten as c = 1 ( C + C ) ( m m ) which geneates a distance metic value of d = T 1 ( m m ) ( C + C ) ( m m ) (7). (8) Since the featue vecto values ae usually not independent, the matix inveses in Eqs. (6) though (8) need to be pefomed using the standad pseudoinvesion pocess. Note that this classification appoach is based on standad discimination theoy [6]. The taining of the algoithms poceeds as follows. The use geneates a database of example image subsets fom the vaious classes, fom which ae geneated example featue vectos. The mean featue vecto and covaiance matix of the featue vectos ae geneated fo each class fom the database of example featue vectos. Fo each pai of classes, the classification vectos ae geneated using Eq. (7). Finally, the theshold values, T, ae geneated by calculating the statistics E[s ], va[s ], E[s ], va[s ] as defined above, assuming the density functions of these vaiables ae Gaussian, and finding the location whee the two density functions intesect. This geneates all of the paametes we need fo the classification: the mean featue vectos fo each class, the classification vectos fo each pai of classes, and the thesholds fo each pai of classes. Since we ae using a M 1 x M 2 window to pefom the classification of the image, fo each placement of the window we assign the esulting class to evey image sample in the window. If we move the window by one sample each time and e-classify, we actually classify each image sample M 1 M 2 times. In the algoithm we eep tac of these individual classifications fo each image sample, and when we have finally left the image sample, we assign it the class that occued most often. This helps to significantly clean up the classification esults nea edges within the image. The classification pocess as defined is not guaanteed to classify evey image sample. It may be that a featue vecto esulting fom some placement of the window does not fall on the ight side of all the hypeplanes fo any single class. If this happens, the algoithm puts the sample into a special No Class categoy. Note that the final classification will only be No Class if this occus most often fo a given image sample ove evey placement of the window. Because the classification vectos ae eigenvectos, they can be vey sensitive to the image values in the taining set. Thus this appoach will only wo well if the taining set is sufficiently lage to be an adequate epesentation of the statistical popeties of each class; that is, thee must be enough samples fom each class to geneate statistically accuate enties in the covaiance matices.

3 Finally, note that if C = C fo any pai of classes, then this appoach is equivalent to a Nomal distibution lielihood atio test. The accuacy of this appoach is detemined by what algoithms ae used to geneate the featue vecto. In ou expeience using this appoach on othe applications, we have seen that a mixtue of statistical measues to estimate tonal popeties and values dawn fom the co-occuence matix [7] to measue textue popeties has woed well. Specifically fo the statistical measues we use E[x], (E[x 2 ]) 1/2, (E[x 3 ]) 1/3, (E[x 4 ]) 1/4, (E[(x-E[x]) 2 ]) 1/2, (E[(x-E[x]) 3 ]) 1/3 and (E[(x- E[x]) 4 ]) 1/4 whee the supescipts ae used to nomalize the values to the same scale, and x epesents the image sample values within the local window. We also use the same statistics divided by the mean, and the same statistics divided by the standad deviation. Thus thee ae 21 statistical measues in the featue vecto. Fo the textue measues we use the standad definition of the co-occuence matix in the liteatue; we fist define a vecto offset of some shift in lines, l, and some shift in columns, c, and then let the cooccuence matix, C o, be defined so that C o (i,) equals the pobability that an image sample value of i and an image sample value of ae sepaated in the image by the vecto ( l, c). Nomally C o is MxM whee M is the numbe of possible diffeent image sample values, but fo this algoithm we combine image samples into 20 diffeent bins and then calculate the co-occuence matix to maintain the size of the matix to 20x20. Note that the co-occuence matix is dependent on the vecto offset used to ceate it. We do not now a pioi what diection o length scale to use fo any given image, so instead we geneate an aveage cooccuence matix ove a seies of vecto offsets. We use vecto lengths of ¼, ½, and ¾ the window width and fo each length geneate an aveage co-occuence matix fo vecto diections of 0, 45, 90, and 135 degees. This geneates thee co-occuence matices to use. Typically what is then done is some metic is calculated on the co-occuence matix that measues the distibutions of pobabilities within the matix. A numbe of standad metics have been defined [7]. We use a standad set of six which ae efeed to as inetia (sometimes called contast), cluste shade, cluste pominence, local homogeneity (sometimes called invese diffeence moment), enegy (sometimes called unifomity) and entopy; see Ref. 7 fo details. Since these values ae extacted fom each of the thee aveaged co-occuence matices, this geneates 18 textue measues fo the featue vecto. Putting these togethe with the statistical measues, the final featue vecto has 39 elements. Open wate finges Solid ice Fig.1. Example of the taining egions used fo the open wate, finge ice, and solid ice classes. Image was collected off the coast of Alasa, CSA 2000 Open wate Rough wate Fig. 2: Examples of the taining egions used fo open wate and ough wate classes. Image was collected off the coast of Alasa in CSA APPLICATION TO ICE REGION LOCATIONS Fo the application of this classification appoach to the poblem of locating ice egions, we wanted to minimize the numbe of classes as much as possible, since we wee not inteested in actually classifying inds of ice, but athe in stictly eliminating ice

4 egions fom the ship detection and wind vecto estimation algoithms. We also assumed that map data could be used to isolate land egions so these wee not consideed. In looing though the images, we decided to divide the poblem into fou classes; finge ice, solid ice, open wate, and ough wate. Figs. 1 and 2 show examples of each ind of class. We divided the ice egions into solid ice and finge ice because visually they had stiingly diffeent tonal and textue qualities. In addition, we needed to teat finge ice egions sepaately because ships often fished between the finges, so we had to pay moe attention to the detailed spatial shapes of these egions. Solid ice almost always occued in blocs, so we did not have the same concen with fine spatial details. We divided the wate egions into two classes, again due to textue diffeences, between vey ough wate caused by atmospheic tubulence (such as convective cells o wind fonts) and othe open wate (which note can also be ough, but which does not have the lage contast featues in the vey ough wate class). Fo the taining set we used RADARSAT-1 ScanSAR Wide Mode imagey with 100 mete sample spacing; theefoe to date this is the only image data fo which the algoithm is applicable. We decided on a 30 x 30 image sample window size (3m x 3m) fo the classification in ode to be able to captue all of the image textue fo each class and manually extacted egions fom the taining set that wee epesentative of each of the fou classes to geneate the featue vectos. We had 832 examples fom solid ice, 624 fom open wate, 31 fom finge ice, and 36 fom ough wate. Note that we wee a little low on the numbe of examples fom finge ice and ough wate fo obust statistics. Table 1 shows the classification esults of applying the algoithm to the taining set. It pefomed vey well in sepaating out the 4 classes, indicating that the appoach was poweful enough to captue the diffeences between ou taining sets. Note that the oiginal goal is to only sepaate ice and wate, thus if we clump the two ice classes togethe and clump the two wate classes togethe, Table 1 indicates that only 1 % of the ice was mis-classified and only 0.5% of the wate. Of couse, pefomance on the taining set is indicative of the ability of the algoithm to sepaate classes; pefomance on data outside of the taining set is usually pooe. To impove opeational pefomance we added a numbe of spatial ules afte classification that too into account nowledge about how ice and wate egions should appea in imagey. To apply these ules, we fist identified all of the connected Table 1: Classification esults fom the fist taining set. Class It Was Put Into Tue Class Solid Finge Open Wate Rough Wate Solid 88% 8% 1% 1% Finge 6% 94% 0% 0% Open Wate 1% 0% 98% 1% Rough Wate 0% 0% 3% 97% blobs in the classified image; i.e. sets of image samples that wee contiguous to each othe and that had been classified into the same class. Fo each blob we identified the numbe of image samples within the blob and the pecentage of peimete samples (i.e. image samples on the edge of the blob) that fell into each othe class. We then applied the following ules. - If a blob was too small (less than 5000 samples) its class was conveted to whateve class the maoity of its peimete samples wee next to. This essentially eplaces small blobs with what was suounding them. - If an ice blob was completed suounded by wate, it was changed to wate. This was because we did not cae about floating o isolated ice egions, and one difficulty the algoithm had was classifying ough wate as ice, which this ule eliminated. - If a wate egion was suounded by ice, it was changed to ice. This was because wate within ice egions would neve contain ships, and neve geneate accuate wind vectos. - Finge ice egions had to have at least 45% of thei peimete samples be eithe solid ice o wate, othewise they wee changed to wate. This ensued that finge ice had to be connected to something that they had gown fom, and not isolated in open wate. Finally, we had to teat finge ice somewhat sepaately due to the fact that we still wanted to detect ships that wee fishing between the finges. Thus fo finge ice egions, we went bac and examine the ada coss section of the image samples. If this value was below a theshold, we conveted that individual pixel into wate. This allowed us to fill in between the finge ice egions with wate. Figs. 3 and 4 show examples of the classification esults. In each figue the top image is the oiginal SAR image, the middle image is the classification map scaled by the SAR image, and the bottom figue is ust the classification map. We show all thee so that the use can see whee wate egions ae indicated next to ice egions.

5 Fig.3. Example of the ice classification esults. Top image is the oiginal SAR image, middle image is the classification map scaled by the SAR image, bottom image is the classification map. Red = solid ice, yellow = finge ice, blue = open wate, geen = land. Image was collected off the coast of Alasa in CSA Fig. 4: Example of the ice classification esults. Top image is the oiginal SAR image, middle image is the classification map scaled by the SAR image, bottom image is the classification map. Red = solid ice, yellow = finge ice, blue = open wate, geen = land. Image was collected off the coast of Alasa in CSA 2000.

6 Table 2: Classification esults fom the second taining set. Class It Was Put Into Tue Class Solid Finge Open Wate Rough Wate Solid 85% 7% 1% 7% Finge 7% 93% 0% 0% Open Wate 0% 0% 95% 5% Rough Wate 7% 0% 9% 84% In geneal the algoithm has pefomed well, howeve we have found some consistent eos in the classification esults as the algoithm was un opeationally. To handle these we have e-tained the algoithm peiodically, adding the egions whee mistaes wee made into the taining set (also eeping the oiginal examples) and e-geneating the algoithm paametes. Table 2 shows the classification esults afte the fist addition to the taining set, the pupose of which was to addess ough wate egions that wee being classified as ice. This taining set had the oiginal 832 samples of solid ice and 624 samples of open wate, but we significantly inceased the ough wate samples to 647 and slightly inceased the finge ice samples to 41. Table 2 shows that in the lage data set the algoithm is having some inceased difficulty sepaating ough wate fom solid ice using these featue metics; note that we now have a 7% misclassification ate vesus the 1% in Table 1. As the sample sizes ae much lage hee fo ough wate, we believe that this is pobably a bette estimate of the algoithm capability than the oiginal taining set. Note though that almost all of these misclassifications get coected when the spatial ules ae applied by eliminating the egions of solid ice that ae suounded by wate. Manual analysis of the final poducts fo a subset of images not in the taining set have shown that all of the mis-classifications have been eliminated. The algoithm is still being tested befoe it goes opeational to detemine if othe eos ae being geneated. 4. SUMMARY To impove the pefomance of automated ship detections and wind vecto estimations in the NOAA/NESDIS Alasa SAR Demonstation Poect, we ae developing an automated algoithm fo locating ice egions so that they can be emoved fom consideation in the ship and wind algoithms. The classification algoithm is a supevised taining algoithm that uses a seies of hypeplanes to sepaate diffeent classes in an n-dimensional featue space. We use a combination of statistical measues and textue metics dawn fom the co-occuence matix to fom the featue vecto. The esults have been encouaging to date; less than 7% mis-classification fom the algoithm with almost no eos afte applying a seies of spatial ules that incopoate what we now about ice and wate egions in the imagey. Testing of the algoithm is ongoing befoe it can become opeational. 5. ACKNOWLEGMENTS This study was suppoted and monitoed by the Office of Reseach and Applications of the National Oceanic and Atmospheic Administation (NOAA) unde ONR Contact N D The views, opinions, and findings contained in this epot ae those of the authos and should not be constued as an official National Oceanic and Atmospheic Administation o U.S. Govenment position, policy o decision. 6. REFERENCES 1. Pichel, W., Clemente-Colón, P. NOAA Coastwatch SAR Applications and Demonstation, John Hopins APL Tech. Dig., vol. 21(1), 49-57, Waceman, C.C., Fiedman, K.S., Pichel, W.G., Clemente-Colón, P. Automatic Detection of Ships in RADARSAT-1 SAR Imagey, Canadian J. Remote Sens., vol. 27, , Monaldo, F.M., Thompson. D.R., Beal, R.C., Pichel, W.G., Clemente-Colón, P. Compaison of SAR-Deived Wind Speed With Model Pedictions and Buoy Measuements, IEEE Tans. Geosc. Remote Sens., vol. 39, , Waceman, C., Mille, D. An Automated Algoithm Fo Sea Classification In The Maginal Zone Using ERS-1 Synthetic Apetue Rada, Eim Technical Repot T, May Waceman, C. Use of An Intefeometic SAR Fo Teain Classification, Poc. 26 th AIPR Woshop, SPIE vol. 3240, 75-86, Lachenbuch, P.A., Disciminant Analysis, Hafne Pess, Haalic, R.M., K. Shanmugam, I. Dinstein, Textual Featues fo Image Categoization, IEEE Tans. Systems, Man Cybe., vol. SMC- 3, , 1973.