CHAPTER 3 FEATURE EXTRACTION AND ACCURACY ASSESSMENT

Transcription

1 64 CHAPTER 3 FEATURE EXTRACTIO AD ACCURACY ASSESSMET Aeral and space mages contan a detaled record of features on the ground at the tme of data acquston. An mage nterpreter systematcally eamnes the mages and, frequently, other supportng materals such as maps and reports of feld observatons. Based on ths study, an nterpretaton s made as to the physcal nature of objects and phenomena appearng n the mages. Interpretatons may take place at a number of levels of complety, from the smple recognton of objects on the earths surface to the dervaton of detaled nformaton regardng the comple nteractons among earth surface and subsurface features. Success n the mage nterpretaton vares wth the tranng and eperence of the nterpreter, the nature of the objects or phenomena beng nterpreted, and the qualty of the mages beng utlzed. In addton, t s mportant that the nterpreter have a thorough understandng of the phenomenon beng studed as well as knowledge of the geographc regon under study. The technques used for mage nterpretaton n ths thess are dscussed n ths chapter. 3.1 FEATURE EXTRACTIO Feature etracton nvolves smplfyng the amount of resources requred to descrbe a large set of data accurately. When the nput data to an algorthm s too large to be processed and t s suspected to be notorously redundant (much data, but not much nformaton) then the nput data wll be transformed nto a reduced representaton set of features. Transformng the

2 65 nput data nto a set of features s called feature etracton. If the features etracted are carefully chosen t s epected that the feature set wll etract the relevant nformaton from the nput data n order to perform the desred task usng ths reduced representaton nstead of the full sze nput. When performng analyss of comple data, one of the major problems stem from the number of varables nvolved. Analyss wth a large number of varables generally requres a large amount of memory and computaton power or a classfcaton algorthm whch over fts the tranng sample and generalzes poorly to new samples. Feature etracton s a general term for methods of constructng combnatons of the varables to get around these problems whle stll descrbng the data wth suffcent accuracy. Snce mage data are by nature very hgh dmensonal, feature etracton s often a necessary step for classfcaton to be successful. Besdes lowerng the computatonal cost, feature etracton s also a means for controllng the so called curse of dmensonalty (Waller and Jan 1978). The purpose of feature etracton s to reduce the orgnal data set by measurng certan propertes or features that dstngush one nput pattern from another. The etracted features provde the characterstcs of the nput type to the classfer by consderng the descrpton of the relevant propertes of the mage nto feature space Tetural Feature Etracton Teture of an mage s characterzed by the attrbute of a pel and ts neghbors over a wndow. In order to capture teture t s requred to fnd out the repettve pattern over the neghborhood of a pel, and hence cooccurrence of the pel attrbute characterzes the teture very well. Haralck et. al. (1973) has shown that the co-occurrence of grey level of the pel can be used for characterzng teture. One dffculty of teture analyss n the past was the lack of adequate tools to characterze dfferent scales of tetures

3 66 effectvely. Recent developments n mult-resoluton analyss (Raja et al 009) such as wavelet transform help to overcome ths dffculty. In ths work, a mult resoluton approach based on wavelet packets for teture analyss and classfcaton s used. The mage s decomposed.e., dvded nto four sub-bands and crtcally sub-sampled by applyng DWPT. The transformed coeffcents n appromaton and detal mages (sub-band mages) are the essental features for the teture analyss and dscrmnaton. As mcro-tetures or macro-tetures have non-unform gray level varatons, they are statstcally characterzed by the features n appromaton and detal mages. Hence, four co-occurrence matrces (C) at dfferent angles = 0 o, 45 o, 90 o and 135 o are derved for orgnal mage, appromaton and detal sub bands of wavelet packet decomposed mage. From these co-occurrence matrces of sze 56 56, sgnfcant WPCFs such as, contrast cluster shade and cluster promnence etc., are computed usng the formulae gven n equatons (3.1) (3.9). The WPCFs, derved from four co- occurrence matrces of orgnal mage, three detal sub-bands of 1-Level wavelet packet decomposed mage and appromaton sub-bands of 1 st and nd Level wavelet packet decomposed mages are averaged and thereby the feature database contans 4 WPCFs (6 7 WPCFs). In addton, the feature database contans the WPSFs such as mean and varance of the orgnal mage. 1 Mean:, j (3.1) 1 j 1 1 Varance: V, j (3.) 0 j 1 Entropy C ( log C(. (3.3) 1 j 1 Contrast C( j ( 0 (3.4) Energy 1 1 C ( j ) (3.5)

4 67 Local homogenety = Cluster shade = n j 0 1 /(1 ( ) C( (3.6) n 3 ( M j M y ) C( (3.7) j 0 Cluster promnence = n 4 ( M j M y ) C( (3.8) j 0 jc( y Correlaton = 1 j 1. (3.9) 3.1. Spectral Feature Etracton y The spectral sgnature of a materal may be defned n the solar reflectve regon by ts reflectance as a functon of wavelength, measured at an approprate spectral resoluton. All spectral reflectance data are unque to the sample and the envronment n whch they are measured. Mneral sgnatures, for eample, wll vary from sample to sample. Vegetaton s even more varable, beng dependent on growth stage, plant health and mosture content. Vegetaton Inde s an ndcator of the presence and condton of green vegetaton. DVI s a commonly used vegetaton nde based on the reflectance propertes of leaves n Red (R) and IR wavelengths. Green plant leaves typcally have low reflectance n the vsble regons of the electromagnetc spectrum due to strong absorpton by leaf mesophyll. Meanwhle, n the near nfrared regon, leaves ehbt hgh reflectance due to etensve scatterng effects n these wavelengths (Tucker and Sellers 1986; Knplng 1970). DVI s based on these propertes and generally provde hgh values for vegetated areas. In addton, DVI helps to compensate for mage varatons caused by changng llumnaton condtons, surface slope and aspect (Lllesand et al 004; Quackenbush et al 000). Therefore, DVI s used to mtgate the shadow effect of hgh-spatal resoluton magery and mprove the classfcaton of vegetated areas.

5 68 ormalzed Vegetaton Inde = (IR-R) / (IR+R) (3.10) DVI for a gven pel always results n a number that ranges from 1 to +1. Generally, non-vegetated areas gve values close to zero and vegetated areas gve values close to one ndcatng the hgh possble densty of green leaves. Therefore, DVI s an effcent nde n dfferentatng vegetaton and non-vegetaton classes. 3. IMAGE GEO-REFERECIG Geo-referencng or geometrc regstraton s the process of assgnng geographc coordnates to data wth any other coordnate system. The geometrc regstraton process nvolves dentfyng the mage coordnates (row and column) of several clearly dscernble ponts, called ground control ponts, n the raw mage and matchng them to ther true postons n ground coordnates (lattude and longtude). Both Ahmedabad and Madura mages were geo-referenced usng four Survey of Inda topographc sheets. The toposheet 46A SW, SE, W, E 1:5,000 was used for Ahmedabad whle 58 Kl SW, SE, W, E 1:5,000 scale was used as reference data for GCPs collecton and valdaton. The standard errors were below 0.5 pel along the X and Y drectons. A sub area of pels was etracted from the orgnal scale, as shown n Fgure ACCURACY ASSESSMET Analyss of Error matr/confuson matr has been one of the most common means of epressng classfcaton accuracy. Error matrces compare, on a category by category bass, the relatonshp between known reference data (ground truth) and the correspondng results of an automated classfcaton (Janssen and Wel 1994). Such matrces are square, wth the

6 69 number of rows and columns equal to the number of categores whose classfcaton accuracy s beng assessed. Ths error matr s prepared for determnng, how well a classfcaton has categorzed a representatve subset of pels (Congalton 1991). Ths matr s obtaned from classfyng the sampled reference ground control pont pels (column) versus the pels actually classfed nto each land cover category by the classfer (rows) (Ftzgerald and Lees 1994). Several characterstcs about classfcaton performance are epressed by an error matr. For eample, one can study the varous classfcaton errors of omsson (ecluson) and commsson (ncluson). All dagonal elements n the matr represent proper land cover classfcaton. And, all the non-dagonal elements represent error of omsson or commsson. Omsson errors correspond to non-dagonal column elements. Commsson errors are represented by non-dagonal row elements (Congalton and Mead 1983). Several descrptve measures can be obtaned from the error matr. They are defned as below. Overall accuracy: It s computed by dvdng the total number of correctly classfed pels (.e., the sum of the elements along the major dagonal) by the total number of reference pels. Lkewse, the accuraces of ndvdual categores can be calculated by dvdng the number of correctly classfed pels n each category by ether the total number of pels n the correspondng row or column. Producer s accuracy (omsson error): It s determned by dvdng the number of correctly classfed pels n each category (on the major dagonal) by the number of reference pels used for that category (the column total).

7 70 User s accuracy (commsson error): It s computed by dvdng the number of correctly classfed pels n each category by the total number of pels that were classfed n that category (the row total). Kappa Coeffcent: The kappa coeffcent s a measure of the dfference between the actual agreement between reference data and an automated classfer and the chance agreement between the reference data and a random classfer. Conceptually, t can be defned as k r 1 r 1 ( (.. ) ) (3.11) where, r s the number of rows n the error matr, s the number of observatons n rows and columns s the total of observatons n row I, total number of observatons ncluded n matr. s the total of observatons n column I and s the 3.4 PREPARATIO OF GROUD TRUTH DATA For a supervsed method of classfcaton t s very mportant to have the ground truth data whch should form the tranng data set and on the bass of whch the real classfcaton can be performed and tested. From the ground truth mage of Ahmedabad, shown n Fgure 3.1, the regons of benchmark data shown n Fgure. are labelled as 4 classes - vegetaton, urban area, water and land area. The vegetaton class ncludes the heavy and sparse vegetaton areas n the mage. The urban area class ncludes buldngs, concrete occurrng n and around, and roads. The water class ncludes patches where there s water n the gven mage ncludng water bodes lke lake.

8 71 Fgure 3.1 Ground Truth Data (Ahmedabad) Ground Truth Data (Madura) About 500 GCPs were used to create the reference data set for the assessment usng e-tre venture global postonng system devce. Some of the GCPs collected durng feld survey of Madura cty are shown n Table 3.1. Table 3.1 Ground Control Ponts ame of the Area Lattude Longtude Elevaton Accuracy (Feet) (Feet) Features Kudhal agar tank Vegetaton Sellur tank Water body Malar nagar Waste land Alagulam tank Water body Reserve lne Urban Burma Colony Urban Perungud Waste land Rng road Waste land Rng road Waste land Chnthaman Urban Melamada Waste land Anna nagar Urban Vandyur stop Urban Vandyur etend Waste land Vaga rver Water body