Five spatial features have been evaluated for their applicability in automatic land use classification of photographic imagery. Data arrays of approximately 10,000 square meters were classified on the basis of an 8 by 8 point data array. Statistical features, information features, sequency features and texture features were evaluated using a distance to prototype classifier and an adaptive classifier. The results indicate approximately 70% accuracy in the classification

Herzog, James H

Rathja, Roy C

English

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Purdue UniversityPurdue e-PubsLARS Symposia Laboratory for Applications of Remote Sensing10-19-1973Comparative Evaluation of Spatial Features inAutomatic Land Use Classification fromPhotographic ImageryJames H. HerzogOregon State UniversityRoy C. RathjaOregon State UniversityFollow this and additional works at: http://docs.lib.purdue.edu/lars_sympThis document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact epubs@purdue.edu foradditional information.Herzog, James H. and Rathja, Roy C., "Comparative Evaluation of Spatial Features in Automatic Land Use Classification fromPhotographic Imagery" (1973). LARS Symposia. Paper 13.http://docs.lib.purdue.edu/lars_symp/13Conference onMachine Processing ofRemotely Sensed DataOctober 16 - 18, 1973The Laboratory for Applications ofRemote SensingPurdue UniversityWest LafayetteIndianaCopyright © 1973Purdue Research FoundationThis paper is provided for personal educational use only,under permission from Purdue Research Foundation.CO~lPi\Rl\TIVl: LVALU1\TION Of SPATIAL FEATURESIN AlJrOHATIC LAT\)D USE CLASSIfICATION fROH PHOTOGRAPHIC IMAGERYJames II. Ilerzog and Roy C. RathjaDepartment of Electrical and Computer EngineeringOregon State UniversityCorvallis, Oregon 97331I. ABSTRACTFive spatial features have been evaluated for their applica-bility in automatic land use classification of photographicimagery. Data arrays of approximately 10,000 square meters wereclassified on the basis of an 8 by 8 point data array. Statisticalfeatures, information features, sequency features and texturefeatures were evaluated using a distance to prototype classifierand an adaptive classifier. The results indicate approximately70% accuracy in the classification.II. INTRODUCTIONThe successful classification of photographic imagery is strongly dependent upon the featureset utilized in the classification and on the classification process itself. In the case of photo-graphic imagery, the process does not accurately p~eserve precision radiometric relationships amongthe entities being classified. As such, features such as point by point optical density have se-vere limitations in thei"!, applicability.Classical human photointerpretation relies heavily on spatial interrelationships. These rela-tionships are often subtle. In the case of shape detection, the computer is not blessed with thehighly diffused parallel processing structure of the human. Shape detection is difficult and costlyon a digital computer.This investigation has chosen to study features which are of intermediate complexity betweenpoint features such as optical density and complex spatial features such as shape. These featureswere studied to determine their usefulness in automatic land use classification of high flightphotographic imagery.The problem selected for this investigation is the classification of land usage in the vicinityof Oregon City, Oregon. Thls region is located in the \¥i1lamette Valley of Western Oregon approxi-mately 20 km (12.5 miles) south of Portland. The Wi1lamette River is vital to the industry, agri-culture, and recreation of the area.Within the region four major land uses were chosen for automatic classification.1) Forest - Mainly Oak, Douglas Fir and Red Alder2) Agriculture - Primarily small grains3) Water resources - Willamette River4) Urban - Residential and manufacturingThe purpose of the investigation was to determine the ability of a computer using a digitizedversion of the photograph to correctly classify the four land use regions using the feature setsdeveloped.This work was .supported in part by the National Aeronautics and Space Administration underContract NAS 5-21831.2A-69III. TIlE SOURCE DATAThe source photograph, Figure 1, for this investigation was obtained on June 22, 1972 by anRB 57 aircraft operated by NASA using a camera system with a 6 inch focal lenoth lens. The ori<Tinalnegative was digitized into 8 bit densities through the cooperation of the University of Oregon'"Chemistry Department. It was later converted to 6 bits for compatibility with data formats beingdeveloped for f:RTS satellite imagery. The negative was digitized at 98.5 points per cm (250 pointsper inch). The distance between digital resolution points was approximately 13 meters (43 feet).IV. 1lIE AUTOMATIC CLASSIFICATION PROBLEMAutomatic classification of land use may be approached using the teclmiqucs of pattern recog-nition. The classical pattern recognition problem is shown in Figure 2.Using the raw data, a feature extractor must first be specified to yield a feature vector forthe raw data set associated with a Classification Unit (CU). Appropriate feature vectors areusually specified based on heuristic algorithms and insight into the nature of the classificationproblem.In this investigation the raw data was grouped into square arrays of 64 data points. Thisresulted in a classification unit of approximately lxl04 meter2 (1.2xl0 5 ft 2) .Five different feature vectors were used in this investigation. Each was evaluated separatelyto gain insight into its applicability to this problem.The classifier receives feature vectors as inputs and produces decisions at its output. Indeveloping the classifier, sets of features corresponding to known CU's are used. One set knownas the training set is used to abstract information in establishing classification rules. Inthis investigation two classifiers were used. The Distance to Prototype classifier uses para-meters derived from the training set to form prototype vectors for each class. The Adaptiveclassifier uses artificial intelligence teclmiques for developing the classification algorithm.Both of these classifiers were evaluated with each of five feature sets.V. FEATURESThe four land use classifications are characterized by the following spatial characteristics:Forest - Homogeneous with a mottled texture.Agriculture - Homogeneous within a field with abrupt intensity changes at field boundaries.-Water - Very homogeneous.Urban - Very non-homogeneous with many intensity changes.A. STATISTICAL FEATURESThe mean value and the standard deviation of the 64 data points in the array were calCUlated.These two features comprise a feature vector Fs which is a measure of average optical density andof the homogeneity of the area.(1)8l:j=la ..1J(2)1 8 8l: l: (a .. -1J)2b3" i=l j=l 1J2A-70(3)(5)(4)(9)(8)H4h(07)h(17)h(27)h(37)h(47)h(57)h(67)h(77)[p]Number of data points of intensity I.IProbability of intensity I.IH =pFInCI. )IThe information feature is based on the entropy of the data array.2A-71D. TEXTURE IThe Texture I features were obtained from a compilation of changes in adjacentwcells. Withthe 8 x 8 data array there are 56 adjacent point differences which can be calculated in both thehorizontal and vertical direction. This gives a total of 112 differences which form a distributionbased on the magnitude of the change. The magnitude of change was arbitrariiy divided into 6B. INFORMATION FEATURESHi of the feature vector was formed by the sum of the squares of the indicated region of the Walshcoefficient matrix shown in equation 8. Each Hi contains information concerning spatial sequency iin the data array. (1)FI is an indication of the distribution of intensities. It, like the standard deviation, is ameasure of the homogeneity of the data. (4)C. SPATIAL SEQUENCY DISTRIBUTIONThe coefficients of the two dimensional lValsh function series were combined to give the Walshfunction equivalent of the conventional Power Spectral Density.The coefficients of the two dimensional Walsh series are given by the matrix operation.H = WTAW (6)where A is the 8 x 8 data array.1 1 1 1 1 1 1 11 1 1 1 -1 -1 -1 -11 1 -1 -1 -1 -1 1 11 1 -1 -1 1 1 -1 -1 (7)W= 1 -1 -1 1 1 -1 -1 11 -1 -1 1 -1 1 1 -11 -1 1 -1 -1 1 -1 11 -1 -1 -1 .1 -1VI. 'lHE CLASSIFIERE. TEXTURE II(13)(11)n 2'1: (fp ' - x.)i=l 1 1J =PdO the number of differences of magnitude a~ .d4 the number of differences of magnitude 4dS the numoer of differences of magni tude S or greater.dOdlF = d2 (10)D d3d4dsFT = d (12)Texture II consists of a single feature. Like Texture I it is based on the differences exist-ing between adjacent data points.A. DISTANCE TO PROTOTYPE~1ean values of each of the feature vectors were computed for each class using the trainingset data. These mean vectors were used as prototype vectors for the class.A distance metric was calculated for an unknown vector X~nd each prototype.Two standard classification teclllliques were used in this four class problem to classify Agri-cultural, Forest, Water, Urban areas based on each of five possible feature vectors described inSection II I.where f pi is the ith component of the prototype for Class P.X was assigned to the class having the smallest distance metric.features. Their composition is related to the scaling of the data and the thresholds selected forthe feature definition. (2)It is common to include a threshold on the distance metric so that if the distance measure isgreater than the threshold, the unknown vector is assigned to a "Don't Know" class.B. ADAPTIVE CLASSIFIERThe adaptive classifier is an example of a nonparametric classification scheme. Initiallynothing need be known about the statistical characteristics of the data.A weight vector Kp is associated with each class in the problem.A function J is then calculated for the augmented feature vector for each class. (Augmentingconsists of attac~ing a 1 as the last entry in the vector.)J p = KpX ( 14)X is then assigned to the class for which the resulting J p is the greatest. Adaptive algorithmsare used to reward or penalize the weight vector depending on the correctness of the classificationof known training vectors.2A-72K = K - aXp pfor a correct classificationfor an incorrect classification(15)(16)The training set was cycled through 25 times with decreasing values of a to get a "best"classification.VII. EXPERIMENTi\L PROCEIlJREThe digitized data was used to construct a line printer gray scale printout using over-strikecharacters. Ivith the aid of ground truth experts training sets and test sets were isolated on boththe line printer output and the original photograph.Sixty classification unitstraining set. Sixty additionalset included field boundaries.cation units.(each of 64 points) were selected for each land use class for theCU's were selected as test sets. In many cases the agriculturalThe five feature vectors were calculated for each of the classifi-The prototype vector was formed from the mean values of the training set data for each class.These prototype vectors were used in the Distance to Prototype classifier. No threshold \~as usedin the classification process.In the Adaptive classifier the Kp vector initially contained a 1 in each location. a waschosen to have an initial value of 1. Feature vectors corresponding to the four different trainingsets were sequentially presented to the classifier. Equations 15, 16 were used to change theweight vector after each classification. The 60 training vectors for each of the four classesresulted in 240 changes in th~ weight vector for a fixed value of a.After the nth pass of the training set the value of a was replaced by l/(n+l) and the processwas repeated. The adjustment and classification was repeated 25 times. At this point classifica-tion results were very stable.VIII. RESULTS AND CONCLUSIONSClassification results are shown in Table 1 and Table 2 for the training set data and test setdata respectively. Inspection of the results from Table 1 and Table 2 indicates that all of thefeatures examined had characteristics suitable for land use classification. Both the parametricand adaptive classifier performed adequately.Urban areas were easily distinguished, in some cases with no errors. The wide dispersion ofoptical density coupled with rapid spatial variation was significantly different than the spatialcharacteristics of the remaining classes.Forest, water, and agriculture were the most difficult to distinguish. Water presents anearly homogeneous texture; forest texture and composition is slightly more varied. The character-istic of the agricultural area is greatly dependent on the crop and location of field lines. Itwas very rare for a sample of these three land uses to be classified as urban.Of special interest is the difficulty of classifying the agricultural samples. The size ofthe classifying array was rather small compared to the field sizes in this region. This resultedin some agricultural samples containing field boundaries and others containing homogeneous vegeta-tion. Adjacent fields often contained different crop composition.In evaluating the effectiveness of each of the features it should be noted that the featurevectors contained a maximum of 6 elements (Texture I) and a minimum of one element (Texture II andEntropy). The IValsh series contained 5 elements and the statistical feature contained two elements.The dimensionality of the feature vector obviously influences the computation time of theclassifiers.The suitability of these features to conditions in which photographic exposure is not accu-rately controlled has not been established. It would appear that all of the features are moresensitive to changes in the optical density of the photograph than to the magnitudewof the densityitself. More work needs to be done in this area.2A-73REFERENCES1. Kawamura, J. G., "Automatic Recognition of Changes in Urban Development from Aerial Photo-graphs," IEEE Transactions on Sys tems , ~Ian and Cybernetics, Vol. SMC-l, July 1971, pp. 230-239.2. LeShack, L. /I..', "N)P of Forest Imagery," Photogrammetric Engineering, August 1971, pp, 885-896.3. Rosenfeld, A. G., "Automatic Recognition of Basic Terrain Types from Aerial Photographs,"Photogranunetric Engineering, t>larch 1962.4. Sutton, R. N., and E. L. Hall, "Texture Measures for Automatic Classification of PulmonaryDisease," IEEl: Transactions on Computers, Vol. C-21, No.7, July 1972, pp. 667-676.5. Wintz, P. A., "Transform Picture Coding," Proceedings of the IEEE, Vol. 60, No.7, July 1972,pp. 809-820.2A-74Table 1. Classification Results of Training Set DataDISTANCE TO MEAN ;..J ADAPTIVE ;..Ju uv V!-< !-<Classified As !-< Classified As !-<0 0u uSTATISTICAL A F IV U0\° A F W U 0'0A 14 34 8 4 23 18 26 3 13 30F 0 60 0 0 100 17 42 1 0 70<J1 W 13 0 47 0 78 5 27 28 0 47U1ro U 0 0 0 60 100 19 0 0 41 68......u Total 75 54INFORMATION A 23 10 16 11 38 8 33 10 9 13F 14 41 5 0 68 0 59 1 0 98'J) W 4 5 51 0 85 0 28 32 0 53'J)ro U 0 0 0 60 100 0 0 0 60 100......u Total 73 66TRANSFORM A 10 3'5 8 7 17 35 7 12 6 58F 0 60 0 0 100 8 40 9 3 67'JJ W 31 0 29 0 48 9 6 44 1 73u:ro lJ 0 0 0 60 100 2 0 0 58 97......u Total 66 ' 74TEXTURE I A 15 18 24 3 25 41 8 11 068F 14 42 4 0 70 23 33 4 0 55U1 W 5 6 49 0 82 25 4 31 0 52Vlro U 0 0 0 60 100 0 0 0 60 100......u Total 69 69TEXTURE II A ~7 9 26 3 37 29 11 146 48F 27 22 11 0 37 43 13 4 0 22'J) W 1 6 53 0 88 25 1 34 0 57U1'"U 0 () 0 60 100 0 0 0 60 100......u Total 65 572A-75Table 2. Classification Results of Test Set DataDISTANCE TO ~IEAN'"'ADAPTIVE ...u oCll CllClassified As !-< ;.;!-< Classified As !-<0 8uSTATISTICAL A F IV U 0,0 A F IV lJ ,,0A 19 12 13 16 32 26 9 2 23 43F 0 60 0 0 100 17 43 0 0 72'" IV 16 0 44 0 73 6 32 22 0 37~ U 0 0 0 60 100 25 0 0 35 58GTotal 76 53INFOR~lATION A 27 6 7 20 45 12 28 4 16 20F 15 33 12 0 55 0 59 1 0 98<Jl IV 5 11 44 0 73 I 29 30 9 50~ U 0 0 0 60 100 0 0 0 60 100G Total 68 (,7TRANSFORM A 9 13 22 16 15 39 5 6 10 65F 0 60 0 0 lOO 7 37 14 2 62<Jl IV 26 0 34 0 57 17 8 35 0 511<Jl U 0 0 0 60 100 4 '0 55 92ro.-< Total es WuTEXTURE I A 22 22 13 3 37 45 6 b .) -"I.'F 13 41 6 0 b8 24 31 5 0 S2IV 7 6 47 0 78 ~- ,) 33 0 55<Jl _.~<Jl lJ 0 0 0 60 100 () 0 () bO 10001.-< Total 71 70uTEXTURE II A 37 7 13 3 (,2 27 16 6 11 4SF 24 24 12 0 40 45 13 :2 0 ~~<Jl IV 4 10 46 0 77 29 1 30 0 50<Jl U 0 0 0 60 100 0 0 0 bO 100ro.-< Total 70 5·1u2A-76.------1Figure 1 .showingThe source photograph for land use cl assi f i cat iontypical training sets and test sets .RawD,,"Feature) • Classifier •~ Extraction , ~Figure 2. The pattern recognition probl em.2A-77Class i ficationDecision

Comparative Evaluation of Spatial Features in Automatic Land Use Classification from Photographic Imagery

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Comparative Evaluation of Spatial Features in Automatic Land Use Classification from Photographic Imagery

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