Oil Spill Detection: SAR Multi-scale Segmentation & Object Features Evaluation

Transcription

1 Oil Spill Detection: SAR Multi-scale Segmentation & Object Features Evaluation K. Topouzelis *, V. Karathanassi **, P. Pavlakis *** and D. Rokos **** Laboratory of Remote Sensing, School of Rural and Surveying Engineering, National Technical University of Athens ABSTRACT The use of image segmentation and object feature extraction in order to classify SAR image objects into oil spill or other features (oil slick look-alikes), it is widely acceptable in oil spill detection research. For this purpose, a number of features (geometric, surrounding, backscattering, etc.) are usually calculated and introduced in a decision support procedure. The aim of the present study is presentation, analysis and evaluation of the above features in order to produce general rules adequate to identify oil spills in any SAR image. SAR image processing is based on a new multi-segmentation technique. As a first step, image objects in different scales are extracted using the multi-segmentation procedure. Following segmentation, a hierarchical network of image objects is developed, which simultaneously presents object information and fuzzy rules for classification. In experiments implemented in SAR images, the method developed has successfully detected oil spills and look alikes. Texture behavior most contributes to detection (texture characteristics 80%), followed by physical behavior (actual backscatter characteristics 53%, spot surroundings 26 %) and finally geometry behavior (geometrical characteristics 2%). Keywords: SAR, oil spills, hierarchical classification network, knowledge base, object image analysis; multi-scale segmentation. 1. INTRODUCTION Several studies aiming at semi-automatic oil spill detection have been implemented [1,2,3,4,5]. These studies rely on the detection of dark areas, which are objects with a high probability of being oil-spills. Dark areas detection if it is not supported by visual inspection [5,9], prerequisite a threshold wind speed [3,6] sufficient to generate the sea state [7]. The extent of the sea state conditions is consequently included in the estimation of the strength of the contrast signal that an oil spill yields. For making the oil-spill detection methods independent from the wind speed data, methods based on the a) image resulting from spectral texture indexes [8] which represents intrinsic spatial variability of grey level in the neighborhood of each pixel, and b) values of intensity of the pixel to be classified and its four direct neighbors and Neural Networks techniques [10], have been developed. All the above studies do not use speckle removal filters. Despeckle preprocessing, in the international literature, is most used in oil spills transport studies, because they allow detecting oil spill spreading over space and time [11,12]. Once the dark areas are detected, classification methods based on Bayesian and other statistical methods [3], tree classifier [4], or Artificial Neural Networks approach [2,5] are applied for characterizing the dark areas as oil spills or lookalike objects. For this purpose, estimation of a number of spectral and spatial features of the dark areas (geometric, surrounding, backscattering, etc.) is prerequisite. In the relevant studies, classification methods are usually applied only on the dark areas, considering them as objects [3,5], whilst dark areas detection methods are based on pixel-basis processing. The transition from the detection step to the characterization one, needs user interference in terms of masking, coding, and selecting the dark objects in order to * ktopo@mail.ntua.gr; phone ; Fax ; Laboratory of Remote Sensing, School of Rural and Surveying Engineering, National Technical University of Athens, Heroon Polytechniou 9, GR-15780, Greece; ** karathan@survey.ntua.gr; phone ; Fax ; ** * ppavla@ncmr.gr; phone **** rslab@central.ntua.gr; phone ; Fax ; Remote Sensing of the Ocean and Sea Ice 2002, Charles R. Bostater, Jr., Rosalia Santoleri, Editors, Proceedings of SPIE Vol (2003) 2003 SPIE X/03/$

2 proceed to the classification processing. The previous element provides methods more user dependant and time consuming. In the opposite case, pixel-basis methods for both processing steps are used [2], making the oil spill detection procedure computationally cumbersome. In this study an oil spill detection method was developed based on a multi-scale segmentation technique [13] which also creates hierarchical and neighbor relations, and a knowledge base, based on oil spill features. Pixel processing is inherent to an object-basis processing approach, supported by the ecognition image processing software. This makes the method almost automated, since the user does not implement masking, coding and selection of the objects, but it results from the segmentation technique. The problem encountered by this automated approach is that dark objects adjacent to the edges of the image may be cutting up and consequently the estimated geometric features referred to them not to be correct. In this case, the user should not take into consideration the method s decision regarding these objects. The segmentation implemented in this study is small scale and does not require external data such as wind speed. It is applied on intensity SAR image and two more images resulting from speckle removal filters. The last two layers were added in order to introduce spreading and schema information in the segmentation procedure. Hence, well-structured objects are resulted. These objects are then classified in calm (dark areas) and rough objects using a local contrast threshold, which implies the sea state. This threshold, which results from statistical measurements of the intensity image, is the only parameter manually introduced by the user, even automatically calculated. Calm objects are characterized as oil spills or look-alikes through a hierarchical network, which simultaneously presents the features of the objects and fuzzy rules for classification. In this study, a number of features were used and evaluated. For their evaluation each one was applied separately, and its performance was estimated. The developed method was applied on image windows, for reducing the processing time. It was observed that a SAR image requires much more time to be processed totally (approximately 2 hours) than partially (approximately 40 minutes, if the image is cut up to 16 windows) using the same processing unit. For the paper needs, the windows were selected in such a way that possible oil spills not to be adjacent to their edges. On the other hand the quality of the results should be independent on the size of the windows. Thus, windows of different size were used for testing the performance of the method in terms of time requirements and results quality. All the used features referred to objects were proved robust. Only the local contrast threshold, which is based on pixel values of the window was proved significantly dependent on the size of the window. Thoughts on improvements towards this direction are also reported in this study. In the next section (2) the proposed methodology is outlined. In section 3 the implementation of the method is described and examples of the produced results are shown. The evaluation of the results is reported in section 4. Finally, in section 5, conclusions on the method performance and improvements to be implemented in a next work are mentioned. 2.1 General overview 2. METHODOLOGY The methodology developed relies on the ecognition software concepts. In this software the classification technique, follows the approach that important semantic information necessary to interpret an image is not represented in single pixels but in meaningful image objects and their mutual relations [13]. ecognition s object oriented image analysis is based upon contiguous, homogeneous image regions which are generated by an initial image segmentation. All the regions are connected and the image is represented as a network of image objects. A hierarchical network is consequently created representing neighbor and hierarchical relations. The organized image objects carry not only the value and statistical information of the pixels of which they consist, but also information on texture and shape, as well as, their positions within the hierarchical network [14]. Image classification is made classifying the image objects according to a class description organized in an appropriate knowledge base. 2.2 Segmentation - Dark spot objects detection A prerequisite to classification in the object-oriented approach is image segmentation. Segmentation refers to the subdivision of an image into separated regions. The segmentation used in a bottom up region merging technique stating with one pixel objects. It creates image objects on several scales as similar pixels in each level are aggregated to 78 Proc. of SPIE Vol. 4880

3 segments, whereby the increasing heterogeneity over the whole image is minimized. Dependent on the task a trade-off between spectral and spatial homogeneity can be performed. The maximum allowed heterogeneity for the resulting image objects in ecogniton is given by a scale parameter. Modifying the value of scale parameter the size of image object varies [15, 16]. In this study it was observed that a small-scale parameter is required, because oil spills of small size are often met, and borders of big size oil spills are better identified if these are resulted from small objects grouping. In many cases the minimization of spectral heterogeneity leads to branched segments or image objects with a fractal shaped borderline. This effect is even stronger in highly texture data, such as radar data. For this reason, in the present study a special weight to the shape criterion -which is one of the segmentation parameters- is given, in order to avoid fractal shapes (Figure 1a,b,c). Moreover, the segmentation technique has a special advance when is working with more than one layer. Thus, two more layers except for the original intensity SAR image have been used into the segmentation procedure. The first layer resulted from a mean 3x3 filtering of the original image, while the second layer from a despeckle procedure which uses combination of Lee-Sigma filter, and local region filter in several window sizes. Both layers contribute to well-structured objects production (Figure 1d), since SAR signal noise is removed on them. A segmentation layer called herein small was produced representing small scale objects, also called sub-objects. Each subobject knows its relations to its neighbors. In the case that segmentation layers of different scale were produced relations of objects within one scale but also to super and sub scale would be produced [17]. (a) Original image (b) Segmentation without any special weight (c) Segmentation with special weight to the shape criterion (d) Three layer segmentation Figure 1. Different segmentation modes The small-scale objects are then separated into two basic categories: the rough representing sea area, and the calm representing possible oil spills. ecogntion uses a frame for formulating the knowledge base for the classification of image objects called class hierarchy. It contains all classes of a classification scheme in a hierarchical structure form. The inheritance of class descriptions of child classes and the semantic grouping of the classes are the basic relations defined by the class hierarchy [15]. In the case study, for this step, the two classes, rough and calm, are defined as parent classes. The basic principle for this classification is that rough areas are certainly not oil spills as the sea backscatter echo is large enough. On the contrary calm areas represented by low signal values, are possible oil spills due to the damping of the wind-generated gravity-capillary sea surface waves that occurs when they are present. In the case that land is present, it is removed by a masking technique. The sea backscatter echo, which illustrates the sea state, has a high spatial variation. Thus the signal contrast between each possible oil spill and its surroundings is different making their detection a difficult task. In this study, the segmented objects are classified by comparing their relevant statistical parameters with a local contrast threshold value. This value is based on the pixel values of the image window and is calculated by the following algorithm: Proc. of SPIE Vol

4 B = Mean + St. Deviation Mean St. Deviation Mean if mean st. deviaton if mean < st. deviaton Where B is the value of the local contrast threshold, Mean is the mean value, and St. Deviation is the standard deviation of the image window. A classification-based segmentation extraction follows, in which all small objects that form together a continuous dark area or all small objects that form a continuous bright area are merged to single meaningful objects of calm and rough areas, respectively. Classification-based segmentation is a method for producing a new segmentation layer whose objects are generated based on selected knowledge and not on the homogeneity criterion. With this generation all adjacent image objects that belong to the same class are merged into a new object (Figure 2). The produced layer consists the big scale segmentation layer and is considered as a second segmentation layer in the next steps. (a) Original Image 2.3 Oil-spills feature analysis (b) Objects in small scale segmentation Figure 2. Classification based segmentation (d) Objects after classification-based segmentation Each segment classified as calm area, it is considered as possible oil spill and a set of features is calculated. The features constitute standard descriptions applied in oil spill detection [3,5]. Features can be separated into three categories: a) those concerning the physical behavior of the oil spills (backscatter characteristics, location); b) those concerning their geometry behaviour (geometrical characteristics); and c) those concerning the texture behaviour (texture characteristics). Researchers providing several features for every category mostly use physical and geometry behaviour of oil spills and look-alikes. Backscatter characteristics of calm areas and their location (backscatter value, mean difference to neighbours, close to land etc), as well as, geometrical characteristics (area, asymmetry, shape index, etc) have commonly been used to help decisions for dark areas classification. Texture based behavior is a pioneer approach in oil spill detection and a very powerful tool of ecognition multisegmentation approach. Texture applications in ecognition need more than one segmentation layer. Texture usually is calculated for the sub-objects of the small-scale segmentation layer. The texture features can be divided in two groups: 80 Proc. of SPIE Vol. 4880

5 texture concerning the spectral information of the sub objects and texture concerning the form of the sub objects. For the present work the original image layer provides spectral information for texture statistical measurements. Standard deviation of the different mean values of the sub-objects is used. Herein, the standard deviation is computed over homogeneous and meaningful areas in difference to the usual practice of computing standard deviation from the single pixel values (original image values). Shape texture refers to the form of sub-objects. It can be used with a variety of parameters as mean value or standard deviation of the areas of the sub-objects, mean value or standard deviation calculated from densities of the areas of the sub-objects, mean value or standard deviation of the asymmetries of the sub-objects and mean value or standard deviation of the directions of the sub-objects. Here, standard deviation of the asymmetries of the sub-objects is used as it provides better results. Features used in this study are illustrated in Table 1. A more detailed description of the features follows. Physical behaviour (Backscatter characteristics, location) Geometry behaviour (Geometrical characteristics) Texture behaviour (Texture characteristics) Backscatter Value Big areas Layer value texture based on sub-objects Mean Difference to Neighbours Asymmetry Shape texture based on sub-objects Power to Mean Ratio Length to width ratio Close to big areas Shape index Close to land Close to fractal objects Table 1. The features used Features referred to the physical behaviour of the oil spills Backscatter characteristics: Backscatter Value. This is calculated from the values of all pixels of the SAR original image which form an image object. Mean Difference to Neighbours. For each neighbouring object the mean difference multiplied by the shared border length of the object of concern is computed and weighted with regard to the length of the border of the object of concern Power to Mean Ratio. It is defined as the ratio of the standard deviation and the mean value of the object. Location Close to big areas. A calm area close enough to an object classified as big area (geometrical characteristic), is not oil spill. Close to land. Close enough to an object classified as land area there are dark areas sheltered by land. If there is no human structure (oil platform, manufactories, port, etc) these are more possible to be look-alike. Close to fractal objects. A calm area close enough to an object classified as segment with high value of shape index (geometrical characteristic), is not oil spill. This feature aims to eliminate small segments classified as oil spills but they are part of a bigger segment with high fractal characteristics. Features referred to the geometry behaviour of the oil spills Big areas. The area of an image object is the number of pixels forming it. In case that we have a big area, larger enough from a normal oil spill size, it is very possible this object to be calm area and not oil spill. Asymmetry. The lengthier an image object, the more asymmetric it is. To an image object, an ellipse is approximated. It can be expressed by the ratio of the lengths of minor and major axes of this ellipse. Very lengthy objects cannot be classified as oil spill because of this feature. Length to width ratio. This feature enables a distinction between long and thin objects from rather compact ones. Proc. of SPIE Vol

6 Shape index is defined as the border length of the image object divided by four times the square root of its area e A. s =. Shape index is used in order to describe the smoothness of the image object borders. Oil spills 4 A have predictable shape, not very fractal. The more fractal an image object appears, the higher is its shape index. Objects with high values of shape index can be eliminated of being oil spills. Features referred to the texture behaviour of the oil spills Spectral texture based on sub-objects. It is referred to the texture which is based on spectral information provided by the original image layer and calculated as standard deviation of the different mean values of the sub-objects. Shape texture based on sub-objects. It is referred to the form of sub-objects and is calculated as standard deviation of the asymmetries of the sub-objects. 2.4 Classification Oil spills identification Classification in ecogniton is based on fuzzy logic. Each class of a classification scheme contains a class description. Each class description consists of a set of fuzzy expressions allowing the estimation of specific features and their logical operation [13]. A fuzzy rule can have one single condition or can consist of a combination of several conditions which have to be fulfilled for an object to be assigned to a class. The classification of calm objects to oil spills and look-alike is carried out in this step. The initial knowledge base developed for separating rough to calm areas was farther extended. For this purpose the big scale segmentation layer is used. The developed knowledge base uses a masking technique to filter those objects that are not oil spills and appears as dark areas in the SAR image. These were classified as look-alikes. Only the objects characterized as possible oil spills by a rule are candidate to be examined by the next rule. The set up of the rules hierarchy and the features used to separate the classes are displayed in figure 3. The rules used, have been random placed as they express independent characteristics of the oil spills. Any order change does not affect the rule contribution or the total accuracy. For this purpose rules can also be grouped according to features concerning the oil spills physical, geometry or texture behaviour. Figure 3. Class hierarchy 3. IMPLEMENTATION OF THE DEVELOPED METHOD The developed method was applied on an ERS 1 image captured in 1/6/1992 (orbit 4589, frame 2961), as also on an ERS 2 image captured in 2/8/1999 (orbit 22392, frame 2882) representing different sea states. The first image represents a rough sea surface, efficient to produce a strong contrast signal in the presence of oil spills. In the second image, the contrast signal is weak making oil spill detection a quite difficult task even by visual interpretation. The sea state varies spatially in both images. From the experiments implemented it was observed that the size of the image affects significantly the computational time, due to the increased size and complexity of the hierarchical network which is created during the segmentation procedure. An ERS scene (65Mb approximately) requires 2 hours to be processed whilst an image window whose size takes up 4Mb memory, requires 2 minutes. Thus, the method was applied on image windows of 4Mb, 12,4Mb and 16Mb size, testing by this way its performance in terms of time requirements and results quality. The processing time for these windows varied from 2 to 8 minutes. The windows were selected according to the following criteria: 82 Proc. of SPIE Vol. 4880

7 1. Dark areas on windows should not be adjacent on the window edges because their geometric characteristics (area, perimeter, etc.) are false represented; and 2. Windows should represent sea surface of different roughness for the contrast signal having different values in the case that an oil spill is present. Figures 4-8 show (a) the SAR image window, (b) the objects identified as calm, and (c) their classification to oil spills (objects in red color) and look-alikes (objects in light blue color). For evaluation reasons the area of figure 5 is also included in figure 7. (a) (b) (c) Figure 4. SAR image window with several oil spills (a) (b) (c) Figure 5. SAR image window with one oil spill (a) (b) (c) Figure 6. SAR image window with two oil spills of different shape and age Proc. of SPIE Vol

8 (a) (c) Figure 7. SAR image window with oil spills of different size, shape, and spreading (a) Figure 8. SAR image window without oil spills (c) 84 Proc. of SPIE Vol. 4880

9 4. EVALUATION OF THE METHOD The evaluation of the results was performed comparing them with visual photo-interpretation results. The photointerpretation for the windows can be summarized as following: In figure 4 the oil spills observed are old as it is pronounced by their spreading and intensity. Thus borders of oil spills in the central area of the window are difficult to be identified by visual interpretation. In figure 5 a backscatter discontinuity, which is responsible for the three different sea states, is observed. Oil spill is adjacent to each sea state, which affects differently the local contrast signal. Dark elongated linear spots were interpreted as natural slicks. In figure 6 the oil spill in the center on the window is fresh, because it yields weak radar backscattering signal. This oil spill is difficult to be discriminated because the sea state is very calm, which pronounces low wind in the area. Its spreading reveals the wind direction. It also consists of narrow strips which are interpreted as the result of an enhanced tilt modulation. Thus the classification-based segmentation cannot group the primitive elements. In figure 7 are observed several oil spills. Long wave crests of low backscatter signal are also observed. The oil spills spreading is different. This reveals that wind direction wasn t spatially and temporally constant on this area. In Figure 8, two backscatter levels are observed, one in the low left part of the image and another in the up right. Dark areas where basically due to low wind which have been produced by the land observed in the upper left part of the image. In the evaluation frame, the percent contribution of each feature to the oil spills detection was also carried out. This is presented in table 2. Features are independent to each other, that s why their percentage sum is not 100. As it is shown the bigger contribution to the oil spills detection is given by the texture characteristics (80%), the physical characteristics follow with 39% contribution, and finally the geometry characteristics with 2%. Although the backscatter value is not considered as a feature in the oil spills look-alikes classification, its contribution was estimated due to its significant value to the calm areas detection. Because all the calm areas were detected in the processed image windows, its contribution was assign to 100%. Between the texture features, the shape texture was proved the most effective with 87% contribution to the oil spills detection. On the contrary, the big areas feature with 0 % contribution and the shape index with 1 %, were proved the most ineffective to detect oil spills. Physical behaviour Backscatter characteristics Backscatter Value Mean Difference to Neighbours Power to Mean Ratio % Contribution to the oil spill characterization Geometry behaviour Geometrical characteristics % Contribution to the oil spill characterization 100 % Big areas 0% Texture behaviour Texture characteristics Layer value texture % Contribution to the oil spill characterization 73% 32% Asymmetry 6% Shape texture 87% 27% Length to width ratio 3% Location Shape index 1% Close big areas 37% Close to land 6% Close to fractal objects 33% Total 39% Total 2% Total 80% Table 2. Percent of the features contribution to the oil spills detection Proc. of SPIE Vol

10 The part of the knowledge base concerning the classification of the calm areas is not affected by the image window size since it is an object-basis procedure. Unlikely, the part that decides which objects are calm is dependent on the window size. For small differences of the size of the image windows, such as those presented in this study, this was not obvious. This can be shown from figures 3 and 7, as the former is a part of the later and the results are the same efficient. When the developed method was applied on an ERS scene, this weakness was revealed. This is due because calm areas are identified by the use of the local contrast threshold which is based on pixel-basis calculations. Method evaluation is illustrated in table 3. From 141 oil spills identified by the photo-interpretation methods and techniques, 116 are correctly classified by the developed method whilst 25 were false classified as look-alikes. The size of the lost oil spills was very small ranging from 15 to 45 pixels. These can also be considered as small segments of big oil spills. Furthermore from 1615 objects classified as look-alikes by the developed method, 1611 were correctly classified and only 4 objects were oil spills. Sea was correctly identified. Corrected Not Corrected Identified Identified Oil Spills Look-alikes Table 3. Method evaluation 5. CONCLUSIONS In this study an object-basis classification method was developed for the oil spills identification. The method relies on the object network of two segmentation layers. One small-scale segmentation layer presenting sub-objects of spectral homogeneity, and a classification based segmentation layer presenting calm areas. In this network oil spills features are incorporated consisting a knowledge base able to identify oil spills. The method was tested on five image windows. It was attended that window edges not to cut up possible oil spills and consequently affect their geometrical characteristics. Oil spills were efficiently detected (116 out of 141). The 25 not identified by the method oil spills were very small segments of bigger ones. Moreover, the oil spill features were separately evaluated. Among them, that of the texture shape has more successfully detected the oil spills, presenting the higher performance (87%). However, the method should be tested on more SAR images. The features used in this study are not affected by the image window size because they refer to objects. The only parameter dependent on the window size is the local contrast threshold, which depicts the sea state around possible oil spills. This occurs because this threshold is based on the backscattering values of the window pixels. Thus in next work this value should be based on one more segmentation layer. In this layer large-scale objects should represent homogeneous areas of different sea state. REFERENCES 1. A. Martinez and V. Moreno, An Oil Spill Monitoring System Based on SAR Images, Spill Science & Technology Bulletin, 3 (1/2), pp , T. Ziemke, Radar image segmentation using recurrent artificial neural networks, Pattern Recognition Letters, 17, pp , H. Anne, S. Schistad, G. Storvik, R. Solberg and E. Volden, Automatic Detection of Oil Spills in ERS SAR Images, IEEE Transactions on geoscience and remote sensing, 37 (4), pp , M. Kubat, R. C. Holte and S. Matwin, Machine learning for the detection of oil spills in satellite radar images, Machine Learning, 30 (2-3), pp , F. D. Frate, A. Petrocchi, J. Lichtenegger and G. Calabresi Neural Networks for oil spill detection using ERS-SAR data, IEEE Transactions on geoscience and remote sensing, 38 (5), pp , Proc. of SPIE Vol. 4880

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