We present a supervised three-stage classification (labeling) scheme applied to SAR images of polar regions for detecting different sea-ice types. The three-stage labeling procedure consists of: 1) a speckle noise filtering stage, based on a sequence of contour detection, segmentation and filtering steps, which removes SAR speckle noise (and texture information as well) without losing spatial details;2) a second stage providing Bayesian, maximum-a-posteriori, hierarchical (coarse-tofine),

adaptive (data-driven) and contextual labeling of piecewise constant intensity images featuring little useful texture information;and 3) an output stage providing a many-to-one relationship between second stage output categories (types or clusters) and desired output classes. Modules 1) and 2), which demonstrated their validity in several applications in the existing literature, are briefly recalled in the current paper. The proposed labeling scheme features some interesting functional properties when applied to sea-ice SAR images: it is easy to use, i.e. it requires minor user

interaction, is robust to changes in input conditions and performs better than a noncontextual (per-pixel) classifier. Application results are presented and discussed for a pair of SAR images extracted, respectively, from an ERS-1 scene acquired on November 1992 over the Bellingshausen Sea (Antarctica) and from an ERS-2 scene

of the East Greenland Sea acquired on March 1997 when a field experiment by the research vessel “Jan Mayen” was conducted in the same area

Baraldi, A.

Parmiggiani, F.

English

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IL NUOVO CIMENTO Vol. 24 C, N. 1 Gennaio-Febbraio 2001Classification of sea-ice types in SAR imagery (∗)(∗∗)A. Baraldi and F. ParmiggianiISAO-CNR - Via Gobetti 101, 40129 Bologna (Italy)(ricevuto il 26 Ottobre 1999; revisionato l’ 8 Settembre 2000; approvato il 20 Settembre 2000)Summary. — We present a supervised three-stage classification (labeling) schemeapplied to SAR images of polar regions for detecting different sea-ice types. Thethree-stage labeling procedure consists of: 1) a speckle noise filtering stage, basedon a sequence of contour detection, segmentation and filtering steps, which removesSAR speckle noise (and texture information as well) without losing spatial details; 2)a second stage providing Bayesian, maximum-a-posteriori, hierarchical (coarse-to-fine), adaptive (data-driven) and contextual labeling of piecewise constant intensityimages featuring little useful texture information; and 3) an output stage providing amany-to-one relationship between second stage output categories (types or clusters)and desired output classes. Modules 1) and 2), which demonstrated their validityin several applications in the existing literature, are briefly recalled in the currentpaper. The proposed labeling scheme features some interesting functional propertieswhen applied to sea-ice SAR images: it is easy to use, i.e. it requires minor userinteraction, is robust to changes in input conditions and performs better than a non-contextual (per-pixel) classifier. Application results are presented and discussed fora pair of SAR images extracted, respectively, from an ERS-1 scene acquired onNovember 1992 over the Bellingshausen Sea (Antarctica) and from an ERS-2 sceneof the East Greenland Sea acquired on March 1997 when a field experiment by theresearch vessel “Jan Mayen” was conducted in the same area.PACS 92.10 – Physics of the oceans.PACS 92.10.Rw – Sea ice.Acronym listMRGMAP: Modified Refined Gamma Maximum A PosterioriPAC: Pappas Adaptive ClusteringMPAC: Modified PAC(∗) Paper presented at the Workshop on Synthetic Aperture Radar (SAR), Florence, 25-26February, 1998.(∗∗) The authors of this paper have agreed to not receive the proofs for correction.c© Societa` Italiana di Fisica 113114 A. BARALDI and F. PARMIGGIANIHCM: Hard c-MeansSEM: Stochastic Expectation MaximizationRMS: Region Merging Step1. – IntroductionSatellite SAR imagery provides an excellent tool for continuous observation of sea-icein polar regions. These regions are almost in the darkness for half of the year and quiteoften covered by clouds, thus their observation is nearly impossible with optical andthermal sensors.The mapping of sea-ice characteristics has great scientific and economic importance:it is important for weather and climate studies and it has economic relevance for trans-portation and ship routing among ices.In recent years several works have been published on segmentation and classificationof SAR imagery, some of them specifically oriented to sea-ice parameter extraction [1-10].This paper describes a contextual and easy to use three-stage supervised classification(labeling) scheme for sea-ice–type detection in SAR images. This three-stage labelingsequence consists of:1) A pre-processing filtering stage, based on a sequence of contour detection, seg-mentation and speckle filtering steps, capable of removing SAR speckle noise (andtexture information as well) without losing spatial details.2) A Bayesian contextual labeling procedure exploiting an adaptive and multiresolu-tion estimate of system parameters which applies to piecewise constant or slowlyvarying intensity images, i.e. to images with little useful texture information, thatmay be corrupted by an additive white Gaussian noise field independent of thescene. This module is robust to changes in initial conditions and requires few andintuitive user-defined parameters to run.3) An output stage equivalent to a multiple-prototype classifier exploiting a many-to-one relationship between second-stage output categories (types, clusters or labels)and supervised output classes. The relationship between label types and outputclasses is either user-defined (when the user initializes classification stage 2 withsupervised reference vectors) or based on majority voting (when initial referencevectors in classification stage 2 are detected by means of unsupervised clusteringalgorithms). Since this third stage is trivial, it will not be further discussed in therest of this paper.Functional properties of modules 1) and 2), which are taken from the literature, aresummarized in sects. 2 and 3. Section 4 presents the data set to be classified andthe training procedure adopted for the classifier. Application results are presented anddiscussed in sect. 5. Conclusions are reported in sect. 6.2. – First stage: speckle noise removalOur first processing stage consists of a SAR speckle filtering procedure, identifiedas MRGMAP [11], which is a Modified version of the Refined Gamma Maximum APosteriori SAR speckle filter proposed in several states of development in recent years [5-7]. MRGMAP consists of three functional boxes which are processed in sequence andwhose characteristics are summarized below:CLASSIFICATION OF SEA-ICE TYPES IN SAR IMAGERY 1151) A SAR contour detection box, proposed in [7], where speckle noise is considered amultiplicative random process, i.e. speckle is supposed to be fully developed. Sincethe fully developed speckle hypothesis does not hold true across image boundaries,this contour detector employs adaptive (multi-scale, oriented) rectangular maskscollecting local statistics to assess whether these masks cross image boundaries.2) A segmentation box capable of detecting closed image segments out of noncon-nected contour pixels. This procedure is based on a region growing mechanismexploiting geometric information exclusively, i.e. no intensity information is em-ployed here.3) The SAR filter box, which applies the Gamma Maximum A Posteriori SAR specklefilter equation [7] to local areas of increasing size within the segment boundariesdetected at segmentation box 2. The input data to this box are: i) the speckled rawimage; and ii) the segmented image obtained with segmentation box 2. Around acentral pixel of interest, the implemented filter scheme grows a local area that mustbe part of the segment to which the central pixel belongs. Thus, local statistics areextracted within segment boundaries, i.e. within spectrally homogeneous regionsin which the fully developed speckle hypothesis holds.The output product of the MRGMAP module is a de-speckled, slowly varying orpiecewise constant intensity image (i.e. this image features little useful texture informa-tion) where spatial details are preserved (due to speckle low-pass filtering within segmentboundaries). This output image satisfies the functional constraints required by the secondstage of our three-stage classification scheme.3. – Second stage: Contextual labeling for image segmentationThe second stage of our classification scheme employs a Modified version, identifiedas MPAC [12], of the well-known Pappas Adaptive Clustering (PAC) algorithm for imagesegmentation [13]. MPAC differs from PAC in its spectral class-conditional model whereglobal and local (i.e. multi-scale) estimates of intensity averages are employed simulta-neously. Advantages of MPAC with respect to other segmentation algorithms found inthe literature are [12]1) Compared with other (noncontextual) clustering algorithms like Hard c-Means(HCM) [13, 14], MPAC is less sensitive to changes in the user-defined number ofinput clusters as it allows the same region (label) type to feature different intensityaverages in different parts of the image, as long as they are separated in space.2) Although it employs no Markov Random Field model supporting special imagefeatures (e.g., thin lines [15, 16]), MPAC has demonstrated to preserve image de-tails better than i) HCM, which is a hard-competitive, Bayesian, noncontextual,Maximum-Likelihood labeling procedure [12]); ii) Stochastic Expectation Maxi-mization (SEM), which is a soft-competitive, Bayesian, contextual labeling proce-dure) [12]; and iii) PAC.116 A. BARALDI and F. PARMIGGIANIFig. 1. – A) ERS-1 SAR scene over the Bellinghausen Sea (Antarctica) of November 22, 1992(Orbit: 7078, Frame: 5733). B) ERS-2 SAR scene over the Odden Ice Tongue, East GreenlandSea, of March 8, 1997 (Orbit: 9840, Frame: 2115).4. – Data set and classification training procedureThe proposed processing scheme is applied to two test images extracted from twodifferent SAR scenes of polar regions (see figs. 1A and B). The two scenes are geo-referenced by means of the commercial software TeraScan (by SeaSpace, Poway, U.S.A.)and calibrated by means of the European Space Agency SAR image Toolbox.Test image A, 512 × 512 pixels in size (equivalent to a surface area of about 6 × 6km), is taken from an ERS-1 scene of the Bellingshausen Sea (Antarctica) acquired onFig. 2. – Test images A and B exctracted from the SAR scenes of figs. 1A and B. A) fullresolution window, 512 × 512 pixels in size. B) Full resolution window, 1000 × 1000 pixels insize.CLASSIFICATION OF SEA-ICE TYPES IN SAR IMAGERY 117Fig. 3. – Reference data, selected by an expert sea-ice photointerpreter, for the test images offigs. 2A and B. The 3 different gray-levels correspond to the 3 classes. A) C1 (floes), C2 (mixedsea-ice) and C3 (open water); B) C1 (frazil ice), C2 (pancake ice) and C3 (open water).November 22, 1992. Test image B, 1000 × 1000 pixels in size (equivalent to a surface areaof about 12 × 12 km), is taken from an ERS-2 scene of the Odden Ice Tongue region inthe East Greenland Sea acquired on March 8, 1997, at the time when a field experimentby the research vessel “Jan Mayen” was conducted in the same area as part of the EUfunded ESOP-2 Project [17].Our aim is to distinguish sea-ice types: floes, mixed sea-ice and open water (no ice) intest image A (fig. 2A), and pancake ice, frazil ice and open water (no ice) in test imageB (fig. 2B).To evaluate the accuracy of our classification procedure by means of an error (orconfusion) matrix [18], test images A and B are supplied with ground truth data, shownin figs. 3A and B, respectively, selected by an expert sea-ice photointerpreter of the ScottPolar Research Institute, University of Cambridge (U.K.). In deeper detail, in test imageA 55924 (= 46313 + 5198 + 4413) reference pixels are selected for classes C1 (floes), C2(mixed sea-ice) and C3 (open water) respectively, while in test image B 302319 (= 47814+ 94455 + 160050) reference pixels are selected for classes C1 (frazil ice), C2 (pancakeice) and C3 (open water).In both test cases, to train the second-stage of our classifier only three labeled pixelsare interactively selected by an expert photo-interpreter to be used as MPAC’s initialcluster templates, each supervised pixel belonging to class C1 to C3, respectively. Inthe case of test image B, this supervised pixel selection is validated by the “Jan Mayen”ground truth data.5. – ResultsWhen the MRGMAP filtering box is applied to the two test images, which visuallylook very fragmented, it detects a large number of spectrally homogeneous segments. Toreduce segment-based computation time, MRGMAP is provided with a post-processingstep to merge small segments (e.g., whose area is ≤ 4 pixels). In table I, the numberof detected segments before and after the Region Merging Step (identified as RMS) is118 A. BARALDI and F. PARMIGGIANITable I. – No. of segments in output images.Test Image MRGMAP MRGMAP + RMS MPACA 106207 5812 655B 254547 15218 965Table II. – Test image A. Three-stage sea-ice classification with MPAC.Category C1 C2 C3 Misclas. % ErrorR1 45937 358 21 379 0.82R2 1456 3432 310 1766 33.97R3 5 722 3686 727 16.47Total Error 2872 5.13Table III. – Test image B. Three-stage sea-ice classification with MPAC.Category C1 C2 C3 Misclas. % ErrorR1 47683 131 0 131 0.27R2 4694 89454 307 5001 5.29R3 0 4434 155616 4434 2.77Total Error 9566 3.16Table IV. – Test image A. Three-stage sea-ice classification with HCM.Category C1 C2 C3 Misclas. % ErrorR1 45935 360 21 381 0.82R2 1768 2856 574 2342 45.05R3 10 677 3726 687 15.56Total Error 3410 6.09Table V. – Test image B. Three-stage sea-ice classification with HCM.Category C1 C2 C3 Misclas. % ErrorR1 47495 299 20 319 0.66R2 7811 85490 1154 8965 9.49R3 0 6736 153314 6736 4.21Total Error 16020 5.29CLASSIFICATION OF SEA-ICE TYPES IN SAR IMAGERY 119Fig. 4. – A and B, output images of the MRGMAP module applied to the test images of figs. 2Aand B, respectively.reported in the second and third column. The two de-speckled output images are shown infigs. 4A and B. Next, MPAC, provided with three initial reference vectors correspondingto classes C1 to C3, is applied to figs. 4A and B to generate labeled output images 5Aand B, respectively, whose number of segments (equivalent to connected areas featuringthe same label type) is reported in the fourth column of table I.Tables II and III represent the confusion matrices of test images 2A and B, respec-tively, where R1, R2 and R3 are the reference data pixels while acronyms C1, C2 and C3identify the classified data pixels. For result comparison, the same three-stage classifica-tion scheme is employed when its second stage is implemented as the well-known Hardc-means (HCM) non-contextual (per-pixel) algorithm (see tables IV and V). In line witha visual inspection of the labeled output images generated by the two tested classifiers,the quantitative comparison of tables II to V confirms that, by exploiting contextualinformation, MPAC is capable of reducing the well-known salt-and-pepper classificationnoise effect which typically affects non-contextual (per-pixel) classifiers. This result isalso in line with other MPAC applications [12].6. – ConclusionsA three-stage classification procedure, whose blocks are taken from the literature, isemployed to classify sea-ice types in SAR imagery of polar regions.The first processing stage is a speckle filtering block which removes textural informa-tion from the original SAR image while preserving small details (i.e. this block generatesa piecewise constant or slowly varying intensity output image). This output image can besuccessfully labeled through a second stage, named MPAC, which makes use of a spectral,contextual, iterative, hierarchical clustering algorithm for 2-D data (image) segmenta-tion. This second stage preserves small but genuine regions, is quite robust to changesin the number of initial clusters, intuitive to use and effective in reducing the salt-and-pepper classification effect which typically affects non-contextual (per-pixel) classifiers.In the detection of three sea-ice types in two SAR test images, the contextual clusteringclassifier performs 16% and 40% better than a non-contextual classifier.120 A. BARALDI and F. PARMIGGIANIFig. 5. – A and B, final result of the classification procedure applied to the test images of figs. 2Aand B, respectively.Some theoretical weaknesses and limitations of the MPAC algorithm are: i) MPACapplies only to images with little useful texture and additive Gaussian noise, independentof the scene; ii) it is unable to detect outliers which may affect the estimate of spectralparameters. iii) Although it is less sensitive to changes in the user-defined number ofinput clusters than traditional (noncontextual) clustering algorithms, MPAC is still asuboptimal labeling procedure sensitive to initial conditions; therefore, one main issuein the user interaction with MPAC remains the choice of the number of clusters to bedetected.Further applications of the proposed classification scheme for SAR images of polarregions will regard: i) first- and multi-year sea-ice detection; ii) position and character ofthe ice edge in the Marginal Ice Zone; iii) size and dynamics of ice floes; iv) ship routingin sea-ice areas.∗ ∗ ∗The work described in this paper was supported by the Italian Space Agency (ASI)and by the Italian National Programme for Antarctic Research (P.N.R.A.). We are alsograteful to the European Space Agency (ESA) for providing SAR imagery.REFERENCES[1] Sun Y., Carlstro¨m A. and Askne J., Int. J. Remote Sensing, 13 (1992) 2489.[2] Sephton A., Brown L., Macklin J., Partington K., Veck N. and Rees W. G., Int.J. Remote Sensing, 15 (1994) 803.[3] Nezry E., Lopes A., Ducrot-Gambart D., Nezry C. and Lee J.S., IEEE Trans.Geosci. Remote Sensing, 34 (1996) 1233.[4] Smits P. C. and Dellepiane S. G., IEEE Trans. Geosci. Remote Sensing, 35 (1997) 844.[5] Lopes A., Touzi R. and Nezry E., IEEE Trans. Geosci. Remote Sensing, 28 (1990) 992.[6] Lopes A., Nezry E., Touzi R. and Laur H., Int. J. Remote Sensing, 14 (1993) 1735.[7] Touzi R., Lopes A. and Bousquet P., IEEE Trans. Geosci. Remote Sensing, 26 (1988)764.CLASSIFICATION OF SEA-ICE TYPES IN SAR IMAGERY 121[8] Ulaby F. T., Kouyate F., Brisco B. and Lee Williams T. H., IEEE Trans. Geosci.Remote Sensing , 24 (1986) 235.[9] Smith D., Int. J. 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Classification of sea-ice types in SAR imagery

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Classification of sea-ice types in SAR imagery

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