Object-oriented analysis applied to high resolution satellite data.

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1 Object-oriented anaysis appied to high resoution sateite data. Vincenzo BARRILE (*),Giuiana BILOTTA (**) * DIMET - Facuty of Engineering Mediterranean University of Reggio Caabria Via Graziea Feo di Vito Reggio Caabria, Te ITALY vincenzo.barrie@unirc.it ** Arch., externa coaborator of DIMET department Via Graziea Feo di Vito Reggio Caabria ITALY giuianabiotta@ibero.it Abstract: - The aim of this contribute is to examine an appication of Object Oriented Image Anaysis on very high resoution data, on Ikonos images - mutispectra and panchromatic of Bagnara Caabra, in the province of Reggio Caabria. Our objectives are to show as an automatic anaysis as we impemented in a unitary package for segmentation and cassification Neuro Fuzzy with a minima manua participation - can get a good cassification aso in presence of high and very high resoution data of sma cities, where higher is an error possibiity. Key-Words: - Object-Oriented Image Anaysis - Morphoogica Based Segmentation Fuzzy Cassification. 1 Introduction The pixe-oriented anaysis of sateite data as main imit has the acknowedgment of semantic ow eve information, as the amount of energy emitted from the pixe, whie the context does not assume any roe. In the object oriented anaysis [1] the semantic eve is raised: reation rues join space are added, topoogica information and statistics and so the context is defined. Recognition is based on concepts of Mathematica Morphoogy appied to the image anaysis and eements of Fuzzy Logic for cassification. In the expained exampe, was partiay used the software ecognition of Definiens Imaging GmbH, that operates a segmentation of the entire scene on more eves [2], and an experimenta software being competed and reviewed propery, impemented for integrating segmentation and cassification. The segmentation Mutiresoution [3] obtains the automatic creation of vectoria poygons, directy extracted from the raster (with the remarkabe advantage of having therefore a perfect coincidence in the superimposition on raster) and subsequenty the fina cassification predisposing an adapted hierarchy of casses that hod account of the reations between the produced segmentation eves. The aim of this contribution is to examine an appication of Object Oriented Image Anaysis on very high resoution data, particuary on Ikonos images - mutispectra and panchromatic of Bagnara Caabra, in the province of Reggio Caabria. Our objectives are to show as an automatic anaysis with a minima manua participation - can get a good cassification aso in presence of high and very high resoution data of sma cities. A comparison of the resuts obtained with some others cassification agorithms shows a strong improvement of cassification resuts. 2 Mutiresoution Segmentation It is a bottom up region-merging technique starting with one-pixe objects. In subsequent steps, smaer image objects are d into bigger ones. Throughout this custering process, the underying optimization procedure minimizes the weighted heterogeneity nh of resuting image objects, where n is the size of a segment and h an arbitrary definition of heterogeneity. In each step, that pair of adjacent image objects is d which stands for the smaest growth of the defined heterogeneity. If the smaest growth exceeds the threshod defined by the scae parameter, the process stops. Doing so, mutiresoution segmentation is a oca optimization procedure. Spectra or coor heterogeneity is the sum of the standard deviations of spectra vaues in each ayer weighted with the weights for each ayer are used: q h s = c = w c σ 1 c (1) where h s is spectra heterogeneity; q = bands number; σ c = standard deviation of digita number in c spectra band; w c = weight assigned to c spectra band. ISSN: Issue 3, Voume 4, March 2008

2 But the excusive minimization of spectra heterogeneity eads to branched segments or to image objects with a fractay shaped borderine. This effect is even stronger in highy textured data, such as radar data. For this reason it is usefu in most cases to mix the criterion for spectra heterogeneity with a criterion for spatia heterogeneity, in order to reduce the deviation from a compact or smooth shape. Heterogeneity as deviation from a compact shape is described by the ratio of the border ength and the square root of the number of pixes forming this image object. h g _ smooth = (2) n where: h g_smooth = fracta factor of spatia heterogeneity; = border ength; n = number of pixes of the image object.. The second is a compactness factor (h g_compact ) that depends from dimensiona ratio of poygon axis: h g _ compact = (3) b where: h g_compact = compactness factor; = berder ength; b = the shortest possibe border ength given by the bounding box of an image object parae to the raster.. The segmentation agorithm proceeds fusing adjacent poygons beginning from every pixe of the image unti the change of observabe heterogeneity between the two origina poygons and the new generated poygon does not exceed the threshod defined from the customer (scae factor). If the change of heterogeneity aready does not exceed the threshod defined the fusion is effectivey reaized, otherwise the two poygons remain separated. Heterogeneity difference (overa fusion vaue) between resutant object and the two origina poygons is: f = w f Δhs + ( 1 w f ) Δhg (4) where: f = overa fusion vaue; w f = the user defined weight for coor (against shape). For w can be chosen a vaue between 0 and 1, whie 0 and 1 is aso possibe: for w f =1 ony the shape heterogeneity is vaued, and for w f = 0 ony the coor heterogeneity is vaued. The difference of spectra heterogeneity (Δh s ) between the resutant poygon and the two poygons before the is: q Δhs = = w [ ( + )] c c nσ n c obj obj n 1 1σ 1c obj2σ obj2c (5) where: n = resutant poygon pixe number; σ c = standard deviation of digita number in c - spestra band of the resutant poygon; n obj1 = pixe number in the first of the two poygons before the ; σ obj1c = standard deviation of digita number in c -spestra band of the first of the poygons before the ; n obj2 = pixe number in the second poygon before the ; σ obj2c = standard deviation of digita number in c -spestra band of the second poygon before the. Again, the change in shape heterogeneity (Δh g ) caused by the is evauated by cacuating the difference between the situation after and before the. This resuts in the foowing methods of computation for smoothness and compactness: Δ h g = wgδhg _ compact + ( 1 wg ) Δhg _ smooth (6) w g being the user defined weight for smoothness (against compactness). For w can be chosen a vaue between 0 and 1, whie 0 and 1 is aso possibe: for w f =1 ony smoothness is vaued, and for w f = 0 ony compactness is vaued. obj1 obj2 Δhg _ compact = n nobj1 + nobj2 n nobj1 nobj2 (7) Δh g _ smooth = n b n (8) obj1 obj1 b obj1 + n obj2 obj2 b obj2 n being the object size, the object perimeter and b the perimeter of the bounding box The choice of the scae factor aows to caibrate the argeness of the resutant poygons, and its definition is tied to the cartographic scae reference that the customer must obtain. The segmentation process is mutiresoution because, beginning from a same image, is possibe to generate various hierarchica eves of poygons with various scae factors. Reducing the scae factor the poygons generated become more and more sma because smaer it must turn out the spectra variabiity intra-poygons, and whereas increasing the scae factor. 2.1 Segmentation procedures The particuarity of the mutiresoution consists in the existing connection between the poygons of the various hierarchica eves of the segmentation. When a first eve of poygons is generated is possibe to generate n new upper hierarchica eves if the scae factor is arger (greater poygons) or n inferior eves if the scae factor is smaer (smaer poygons). Poygons of inferior hierarchica eve are aways geometricay consisting with those of upper ISSN: Issue 3, Voume 4, March 2008

3 hierarchica eve, so every poygon of inferior eve ony beongs to one poygon of upper eve. A the poygons of the various eves of segmentation constitute an ony database in which are a the existing connections between the poygons of the same or various hierarchica eves. For every poygon are therefore known the poygons in contact on the same hierarchica eve, the poygons that constitute an eventua inferior hierarchica eve and the poygon in which it is contained in the eventua upper hierarchica eve The procedure for the reaization of a good cassification passes through a fine-resoution segmentation creating more segmentation eves, with the parameters indicated in the foowing tabe hoding account of characteristics of the IKONOS dataset with avaiabiity of panchromatic: Leve II has the same weights attributed to the ayers in the eve but parameters of scae are modified from 4 to 10, and for eve IV from 10 to 45. The ast eve of segmentation is the III, visibe ony with a zoom, since scae factor assumes the pixe. The visuaization for pixe in previous eve IV shows that there is maximum heterogeneity. There a course segmentation identifies wider areas unifying subeves objects. It wi foow the cassification procedure. Bands Criteri di omogeneità Shape Settings Segmentation eve PAN RED GREEN BLUE NIR Scae Coor Shape Smoothness Compactness Preiminar Yes No No No No Leve I No Yes Yes Yes Yes Leve II No Yes Yes Yes Yes Leve III No Yes Yes Yes Yes Leve IV No Yes Yes Yes Yes Fig.1: Preiminary eve of segmentation. Tabe 1 The preiminary eve of segmentation works on panchromatic image, has the aim of reaizing objects at the best resoution. The eve I characterizes the main casses. Leve II must be more detaied for it is based on the reations with the subeve. Leve IV instead has to cassify great areas based on the city density in subeves. At ast eve III it takes advantage of the reations of a the three described eves in order to obtain the refined and corrected fina cassification with a minimum of unskied abour. The preiminary segmentation eve is reaized with the parameters of homogeneity enunciated, and operating the preiminary segmentation on the panchromatic image using the parameters in tabe 1. This segmentation makes objects at the best resoution, based on the mutispectra dataset bue, green, red and nir, in a composition 4,3,2 and has the eve of segmentation on the added ayers assigning weight 0 to ayer pan and 1 to others. It corresponds to the eve of the tabe and has the same parameters aready used. Fig.2: Leve 1: zoom. ISSN: Issue 3, Voume 4, March 2008

4 Fig.3: Segmentation, eve II. Fig.6: Leve IV, pixe visuaization. This segmentation is partiay known in iterature (software ecognition of Definiens Imaging GmbH). In our job we propose the integration of segmentation and cassification Neura Fuzzy in a unitary package for fina cassification of image. Fig.4: Segmentation, eve IV. Fig.5: Segmentation, eve III. 3 Fuzzy Cassification Fuzzy ogic, a mathematica approach to quantifying uncertain statements, repaces the two stricty ogica statements yes and no by the continuous range of [0 1], where 0 means exacty no and 1 means exacty yes.. A vaues between 0 and 1 represent a more or ess certain state of yes and no. More recenty, fuzzy sets have been empoyed in the cassification of and terrain covers [9]. Fuzzy sets theory, [10] which has been deveoped to dea with imprecise information, can provide a more appropriate soution to this probem. In fact, it provides usefu concepts and toos to dea with imprecise information. It aows each region to have partia and mutipe membership in severa casses. A region s membership grade function with respect to a specific cass indicates to what extent its properties are akin to that cass. The vaue of the membership grade varies between 0 and 1. Coser the vaue is to 1 more that region beongs to that cass. Partia membership aows that the information about more compex situations, such as cover mixture or intermediate conditions, can be better represented and utiized. Fuzzy ogic emuates human thinking and takes into account even inguistic rues. Fuzzy cassification systems are we suited to handing most vagueness in remote sensing information extraction. Parameter and mode uncertainties are considered as using fuzzy sets defined by membership functions. Instead of the binary true and fase mutivaued fuzzy ogic aows transitions between true and fase. ISSN: Issue 3, Voume 4, March 2008

5 There are more or ess strict reaizations of the ogica operations and or or. The output of a fuzzy cassification system is a fuzzy cassification, where the membership degree to each and cover or and use cass is given for each object. This enabes detaied performance anaysis and gives insight into the cass mixture for each image object. This is a major advantage of soft cassification. The maximum membership degree determines the fina cassification to buid an interface to crisp systems. urban water vegetation = 0.6, = 0.8, = 0. 3 Fig. 7: Rectanguar and trapezoida membership functions on feature x to define crisp set M (yeow) M (X) and fuzzy set A (orange) A (X) over the feature range X;. Fig.10: Fuzzy cassification for the considered and cover casses Urban, Water and Vegetation. The equa membership degrees indicate an unstabe cassification between these casses for the considered image object. If the and cover casses can usuay be distinguished on the data set, this resut indicates that a and cover casses are within the image object to a simiar degree. The high membership vaue shows that the assignment to this cass mixture is reiabe urban water vegetation = 0.8, = 0.8, = 0. 8 Fig. 8: The membership functions on feature x define the fuzzy set ow, medium and high for this feature. Fig. 9: Fuzzy cassification for the considered and cover casses urban, water and vegetation. The image object is a member of a casses to various degrees: Fig. 11: Fuzzy cassification for the considered and cover casses Urban, Water and Vegetation. The equa membership degrees again indicate an unstabe cassification between these casses for the considered image object, as in fig. 4. However, the ow membership vaue indicates a highy unreiabe assignment. Assuming a threshod of a minimum membership degree of 0.3, no cass assignment wi be given in the fina output. ISSN: Issue 3, Voume 4, March 2008

6 urban water vegetation = 0.2, = 0.2, = 0. 2 A fuzzy rue base is a combination of fuzzy rues, which combine different fuzzy sets. The simpest fuzzy rues are dependent on ony one fuzzy set. Fuzzy rues are if then rues. If a condition is fufied, an action takes pace. Referring to fig. 8, the foowing rue coud be defined: If feature x is ow, then the image object shoud be assigned to and cover W. In fuzzy terminoogy this woud be written: If feature x is a member of fuzzy set ow, then the image object is a member of and cover W. According to the definition in fig. 8, in case feature vaue x = 70, the membership to and cover W woud be 0.4, in case x = 200, the membership to and cover W woud be 0. To create advanced fuzzy rues, fuzzy sets can be combined. An operator returns a fuzzy vaue that is derived from the combined fuzzy sets. How this vaue is derived depends on the operator. The basic operators are and and or. and represents the minimum, meaning that the minimum vaue of a sets defines the return vaue. or represents the maximum vaue, meaning that the maximum vaue of a sets defines the return vaue. The resuts are very transparent and ensure independence of the sequence of ogic combinations within the rue base (A and B gives the same resut as B and A). In addition a hierarchic structure foowing common ogic (e.g., A or (B and C) equas (A or B) and (A or C)) can be created easiy. A fuzzy rue base deivers a fuzzy cassification, which consists of discrete return vaues for each of the considered output casses (see fig. 9). These vaues represent the degree of cass assignment. Must be considered that fuzzy cassification gives a possibiity for an object to beong to a cass, whie cassification based on probabiity give a probabiity to beong to a cass. A The higher the return vaue for the most possibe cass, the more reiabe the assignment. In the exampe above, the membership to water is rather high and in most appications this object woud therefore be assigned to the cass Water. The minimum membership vaue an object needs to have in order to be assigned to a cass can be defined. Fuzzy cassification with its enhanced anaysis of cassification performance of cass mixture, cassification reiabiity and stabiity is possibe, because fuzzy cassification is one powerfu approach to soft cassification. The resuts of the fuzzy cassification are an important input for information fusion in current and future remote sensing systems with mutidata sources. The reiabiity of cass assignments for each sensor can be used to find the most possibe and probabe cass assignment. A soution is possibe, even if there are contradictory cass assignments based on different sensor data Neura fuzzy systems Neura fuzzy systems of prediction (SIF) and neuro fuzzy (RNF) are known in iterature. Our contribution is the creation of an integrated package impementing totay the cassification and partiay the segmentation. As known, the Neura Nets are born from the idea of being abe to get inteigence and abiity to decision. Substantiay these are adaptive systems earning, through exampes, to execute correcty a determined task invoving compex, not inear and mutivariabe reations. Our net can be outined ike a back-box that has inside some neurona ayers: the first of them is the input ayer, and is made from many neurons how many are the incomes of the system; then there are from 0 to 2 inner ayer; a Neura Net with 2 inner ayer can approximate whichever type of function; at the end there is an output ayer, with many neurons how many are the escapes of the system. The neurons are connected each other by synaptic inks (Inter and intra-ayer) having some opportune weights, whose vaue is used in the earning agorithm. The Neura Net, therefore, is a system Mutipe Input Mutipe Output (MIMO). Referring to the wide bibiography for an exhaustive description of the Neura Nets, we can say that a cassic exampe of Net Neura is Muti-Layer Perceptron, trained with the Backpropagation agorithm. The signa object of predictive anaysis is found and processed; the fina numerica series is the samped version. It works subsequenty in the simuation environment. The predictive systems based on MLP Neura Nets are strongy used in iterature. These do not need of pre-cognized information on the structure of the mode characterizing the phenomenon to anayze; therefore they are idea for systems that must be processed with a ow eve of acquaintance. The Neura Nets are therefore usefu for functiona predictions and modeing systems where the physica processes are not known or are highy compex. So their use is simpe and highy suitabe for phenomena whose compexity and/or structuring are known, for this acquaintance is usefu to the research worker structuring Neura Net. Is aready common knowedge that a RNF consist in the representation of a Fuzzy system through a Neura Net. This tendency deveoped in the ast ISSN: Issue 3, Voume 4, March 2008

7 years. In fact, if a Fuzzy system is fused with a neura net and if are avaiabe (from backpropagation to the other genetic agorithms), effective methods of earning for the net, then the Fuzzy system become very more efficient. Regarding deveop environments for traditiona Fuzzy systems, this approach suppies the possibiity to reaize systems that "they earn", taking advantage of a deveoped the theoretica and appicative baggage avaiabe for Neura Nets. The inspirer idea, taking advantage of the toerance towards uncertainty and imprecision, is the possibiity of sturdier soutions at a ow cost. This contribute don t give an exhaustive description of the Fuzzy ogic, for which we refer to wide bibiography in scientific iterature; but it wants to emphasize a difference between Fuzzy ogic and conventiona ogic (binary). 4 Resuts of the agorithm proposed As previousy expained, our contribution is the creation of an integrated package impementing cassification (RNF) and segmentation (as we described in previous chapter) usefu for automating cassification procedure, getting higher accuracy for appications ike that we are now proposing. Of course an automated package is very usefu for processing great many data in a short time. We carried out a RNF cassification on a segmentation with a preiminary eve on the panchromatic image and four eves on the mutispectra data, obtaining a first eve of cassification in four casses. We intend to make a carefu study for obtaining a cassification in other three eves, getting it deeper unti and 23 casses. Fig. 12 Cassification Fig. 13 Cass Hierarchy: at the first eve. We compared confusion matrix of our Fuzzy Set with some cassification agorithms as Mahaanobis Distance, Maximum Likeihood, Neura Net. Tabe 2 shows how Neura Fuzzy Set has the higher accuracy and the best resuts. Mahaan. Distance Agorithm Maximum Likeihood Neura Net Neura Fuzzy Set Overa Accuracy % % % % Uncassified impervious 103/ / / /149 water 112/ / / /135 cass agricuture 81/ / / /141 rura 66/ / / /124 Tota 362/ / / /549 Tabe 2 The future perspectives expect to refine the cassification. As described previousy, we are reaizing a Cass Hierarchy structured, on the base of the nomencature indicated previousy, on 4 eves respective with 4, 12, 18 and 23 casses 5 Concusion Cassification and mutiresoution segmentation object-oriented techniques distinguish structura methodoogy from cassic spectra anaysis. The pixe-oriented anaysis increases ambiguity in statistics definition of the and use casses increasing resoution in remote sensing images. With objectoriented anaysis is instead possibe to using better information from remote sensing data with an immediate integrabiity in the GIS aowing the direct reaization of vectoria maps, the used software, ecognition of the Definiens Imaging, that it appies to concepts of Mathematica Morphoogy and principes of ogic fuzzy, organizes the data hierarchicay and it concurs to arrange of different typoogy, integrating aso raster and vectoria data. The possibiity to introduce rues for the ocation of the context and the reations between the objects meaningfuy increases the acknowedgment possibiity automatic rife of the objects on the and surface. Aso imitating therefore the approach foowed in manua photo interpretation, such methodoogy exceeds the imits of a subjective cassification, by making a process that can be ISSN: Issue 3, Voume 4, March 2008

8 reproduced and homogenous, and exceeds the probems of the traditiona cassification techniques. References: [1] Benediktsson J. A., Pesaresi M., Arnason K, Cassification and Feature Extraction for Remote Sensing Images From Urban Areas Based on Morphoogica Transformations, IEEE Transactions on Geoscience and Remote Sensing, Vo. 41, No.9, [2] Baatz M., Benz U., Dehgani S., Heynen M., Hötje A., Hofmann P., Lingenfeder I., Mimer M., Sohbach M., Weber M., Wihauck G., ecognition 4.0 professiona user guide, Definiens Imaging GmbH, München, [3] Köppen M., Ruiz-de-Soar J., Soie P., Texture Segmentation by bioogicay-inspired use of Neura Networks and Mathematica Morphoogy, Proceedings of the Internationa ICSC/IFAC Symposium on Neura Computation (NC 98), ICSC Academic Press, Vienna, 1998, pp [4] Pesaresi M., Kaneopouos J., Morphoogica Based Segmentation and Very High Resoution Remotey Sensed Data, in Detection of Urban Features Using Morphoogica Based Segmentation, MAVIRIC Workshop, Kingston University, [5] Serra J., Image Anaysis and Mathematica Morphoogy, Vo. 2, Theoretica Advances, Academic Press, New York, [6] Shackeford A.K., Davis C.H., A Hierarchica Fuzzy Cassification Approach for High Resoution Mutispectra Data Over Urban Areas, IEEE Transactions on Geoscience and Remote Sensing, Vo. 41, No.9, [7] Sma C., Mutiresoution Anaysis of Urban Refectance, IEEE/ISPRS joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Roma, [8] Soie P., Pesaresi M., Advances in Mathematica Morphoogy Appied to Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, Vo. 40, No.9, [9] Y.Chang Tzeng et a, A Fuzzy Neura Network to SAR Image Cassification, IEEE Transaction on Geoscience and Remote Sensing, Vo. 36, 1998, pp [10] L. A. Zadeh, Fuzzy Sets, Information Contro, Vo.8, 1965, pp [11] 01. Angiui G., Barrie V., Caccioa M., M- SVM SAR Images Cassification: Experimenta Resuts and Vaidations, Rivista Itaiana di Teerievamento, Vo 32, 2005, pp , ISSN [12] 02. Angiui G., Barrie V., Caccioa M., SAR Imagery Cassification Using Muti-Cass Support Vector Machines, JEWA, vo. 19 n. 14, 2005, pp , ISSN and Proceedings of PIERS 2005, Hangzhou, China, August 2005, pp , ISBN: [13] 01. Barrie V., Caccioa M., Versaci M., A Minima Fuzzy Entropy Mode for Pattern Recognition: Evauation in a SAR Imagery Appication, Proceedings of 5th WSEAS Internationa Conference on Artificia Inteigence, Knowedge Engineering, Data Bases, AIKED 2006, Madrid, Spain, February 2006, pp , ISBN [14] 02. Barrie V., Caccioa M., Versaci M., Fuzzy Cassification with Minima Entropy Modes to Sove Pattern Recognition Probems: a Compared Evauation in SAR Imagery, WSEAS Transactions on Information Science and Appications, Issue 4, vo. 03, 2006, pp , ISSN WSEAS Society. [15] Barrie V., Caccioa M., Minniti C., Versaci M., Remote Detection of Cerebra Pathoogies in Magnetic Resonance Imagery: an Unsupervised Heuristic Approach, in H.-G. Maas and D. Schneider eds. Internationa Archives of Photogrammetry, Remote Sensing and Spatia Information Sciences, IAPRS Vo. XXXVI, Part 5, pp , ISSN ISPRS Society and Proceedings of ISPRS Commission V Symposium Image Engineering and Vision Metroogy, Dresden, Germany, September 2006 [16] Barrie V., Biotta G., Metodoogie Strutturai su immagini Sateitari per anaisi Urbana e Territoriae XI ASITA 2007, Torino (Itay) pp ISBN ISSN: Issue 3, Voume 4, March 2008