The Classification using the Merged Imagery from SPOT and LANDSAT

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1 The Classfcaton usng the Merged Imagery from SPOT and LANDSAT In-Joon Kang *, Hyun Cho *, Yong Ku Chang *, Jong-Chul Lee ** * Dept. of cvl engneerng, Pusan Natonal Unv., Pusan, , S.Korea Ijkang@pusan.ac.kr ** Dept. of cvl engneerng, Pukyung Natonal Unv. Pusan, , S.Korea KEY WORDS : TM, SPOT, Neural Networks, MLC. ABSTRACT Several commercal companes that plan to provde mproved panchromatc and/or mult-spectral remote sensor data n the near future are suggestng that merge datasets wll be of sgnfcant value. Ths study evaluated the utlty of one major mergng process-process components analyss and ts nverse. The 6 bands of 30 30m Landsat TM data and the 0 0m SPOT panchromatc data were used to create a new 0 0m merged data fle. For the mage classfcaton, 6 bands that s st, 2nd, 3rd, 4th, 5th and 7th band may be used n conjuncton wth supervsed classfcaton algorthms except band 6. One of the 7 bands s Band 6 that records thermal IR energy and s rarely used because of ts coarse spatal resoluton (20m) except beng employed n thermal mappng. Because SPOT panchromatc has hgh resoluton, t makes 0 0m SPOT panchromatc data be used to classfy for the detaled classfcaton. SPOT as the Landsat has acqured hundreds of thousands of mages n dgtal format that are commercally avalable and are used by scentsts n dfferent felds. After the merged, the classfcatons used supervsed classfcaton and neural network. The method of the supervsed classfcaton s what used parallelepped and/or mnmum dstance and MLC (Maxmum Lkelhood Classfcaton). The back-propagaton n the mult-layer percepton s one of the neural networks. The used method n ths paper s MLC (Maxmum Lkelhood Classfcaton) of the supervsed classfcaton and the back-propagaton of the neural network. Later n ths research SPOT systems and mages are compared wth these classfcaton. A comparatve analyss of the classfcatons from the TM and the merged SPOT/TM datasets wll be resulted n some conclusons. As a result, the overall accuracy at the MLC of the Mergng Image was % wth a KHAT of and t at the MLC of only TM was %, whle a KHAT was But the overall accuracy at the B.P of the neural networks was 87.78% wth a KHAT of 0.84 and the TM was %, whle a KHAT was INTRODUCTION Classfcaton of remote-sensng data has tradtonally been performed by classcal statstcal methods (e.g., bayesal and K-nearest-neghor calssfers). In recent years, the remote-sensng communty has become nterested n applyng neural network to data classfcaton. Though many types of neural network models could be appled to remote-sensng data classfcaton, most of the research work deals wth few of them. One of the most wdely used neural models s the Back-Propagaton algorthm (BP). A study by Hepner et al. (989) concluded that neural networks (NN) could map general land-cover types (Anderson Level I) wth greater accuracy than a conventonal maxmum-lkelhood classfer when usng Landsat Thematc Mapper data. A.K Skdmore et al. (997) concluded that the neural-network approach does net offer sgnfcant advantages over conventonal classfcaton schemes for mappng eucalypt forests from Landsat TM and Ancllary GIS data at the Anderson Level III forest type level. X.long et al. (999) studed remotely sensed change detecton based on artfcal neural networks It s concluded that the traned four-layered neural network was able to provde complete categorcal nformaton about the nature of changes and detect land-cover changes wth an overall accuracy of 95.6 percent for a four-class (.e, 6 change classes) classfcaton scheme. The man objectve of ths study was to compare MLC(Maxmum Lkelhood Classfcaton) of the supervsed classfcaton wth the back-propagaton of the neural network of the Merged Imagery from SPOT and LANDSAT. 2. Artfcal Neural Networks(ANNs) 640 Internatonal Archves of Photogrammetry and Remote Sensng. Vol. XXXIII, Part B7. Amsterdam 2000.

2 Work on artfcal Neural Networks, commonly referred to as neural networks, has been motvated rght from ts ncepton by the recognton that the human bran computes n an entrely dfferent way from the conventonal dgtal computer. The bran s a hghly complex, nonlnear, and parallel computer (nformaton-processng system). It has the capablty to organze ts strutural socsttyents, known as neurons, so as to perform certan computatons (e.g., pattern recognton, percepton, and motor control) many tmes faster than the fastest dgtal computer n exstence today. The general methods of ths Study have been used by the back-propagaton of the neural network and MLC of the supervsed classfcaton. Frst, The Back-propagaton s one of the most mportant hstorcal developments n neural networks. 2.. Back-propagaton of the neural networks A Back-propagaton algorthm was mplemented for a three-layer network (see Fg..) consstng of an nput, hdden, and output layer because most comparable studes used the back-propagaton algorthm, or a dervatve of the backpropagaton, so ts use allows a comparson wth these results; and dscussons wth experenced colleagues revealed a consensus that the back-propagaton algorthm s generally applcable and has good modelng capabltes. The backpropagaton algorthm comprses a forward and a backward phase through the neural-network structure. The frst phase s forward, durng whch the values of the output nodes are calculated based on the GIS and remotely sensed data values nput to the neural network. In the second phase, the calculated output mode values are compared wth the target values. The dfference between the value calculated for the node and the value of the target mode s treated as the error; ths error s used to modfy the weghts of the connectons n the prevous layer. Ths represents one epoch of the backpropagaton algorthm. In an teratve process, the output node values are agan calculated, and the error s then propatated backwards. The total error n the system s calculated as the root-mean-square error between the calculated value and the target value for each mode. The algorthm contnues untl the total error n the system decreases to a prespecfed level, or the rate of decrease n the total system error becomes asymptotc. A bref descrpton of the backpropagaton algorthm now follows. Let us consder a three-layer network as shown n Fg. to llustrate the detals of the back-propagaton learnng algorthm. The result can be easly extended to networks wth any number of layers. In ths Fg., we have m nodes n the nput layer, l nodes n the hdden layer, and n nodes n the output layer; the sold lnes show the forward propagaton of sgnals, and the dashed lnes show the backward propagaton of errors. v z w z y x zj z yj xj xm zp z yn Fg. Three-layer back-propagaton neural network. In summary, the error back-propagaton learnng algorthm can be outlned n the followng algorthm BP Consder a network wth feedforward layers, q=,2, and let q net and q y denote the net nput and output of the th n the qth layer, respectvely. The network has m nput nodes and output nodes. Let q w j denote the q connecton weght from y and q y. (k ) Input => A set of tranng pars {( x, k elements as, that, x =. m+ (k ) d ) k=,2, p}, where the nput vectors are augmented wth the last Internatonal Archves of Photogrammetry and Remote Sensng. Vol. XXXIII, Part B7. Amsterdam

3 Step => Intalzaton : Choose ñ >0 and E max (maxmum tolerable error), Intalze the weghts to small random values. Set E=0 and k=. Step 2 => Tranng loop : Apply the kth nput pattern to the nput layer (q=): = x for all q l y y = (k ) Step 3 => Forward propagaton: Propagate the sgnal forward through the network usng q q q q y = a( net ) = a( w y j ) j j for each I and q untl the outputs of the output layer have all been obtaned. Step 4 => Output error measure : Compute the error value and error sgnals δ for the output layer: Step 5 => Error back-propagaton: Propagate the errors backward to update the weghts and compute the error sgnals q δ for the precedng layers: Step 6 => One epoch loopng : Check whether the whole set of tranng data has been cycled once. If k <p, then k=k+ and go to step ; otherwse, go to step 7. Step 7 => Total error checkng : Check whether the current total error s acceptable :If E < E max, then termnate the tranng process and output the fnal weght; otherwse, E=0, k=, and ntate the new tranng epoch by gong to step. End BP q δ E δ = 2 = n = ( d ( d ( k ) ( k ) y y ) a '( ) 2 + net E, q q q q new q old q w j = η δ y and w, j = w j + w j q q q = a '( net ) w δ j j for q =,,,2. ). 3. STUDY AREA The study area for ths project s GADUC Island area n S.Korea that s les wthn the Unversal Transverse Mercator (UTM) Zone 52, and ranges n UTM coordnates from 80, meters west to 87, meter east and from meters north to 65, meters south. The TM mages were from May 997 and The SPOT mage was the same as TM mages. Both mages were cloud free over the study area. Fg 2. The study Area on Gaduc Island 642 Internatonal Archves of Photogrammetry and Remote Sensng. Vol. XXXIII, Part B7. Amsterdam 2000.

4 4. THE MERGED METHODS There are several possble approaches to mergng the data: The Intensty-Hue-Saturaton (IHS) Transform Substtutes hgh spatal resoluton magery for the ntensty component of the low spatal resoluton magery. The bravery transform uses a rato algorthm to merge the dfferent mages. The multplcatve method s base on the theory of the mage Prncpal Component and Inverse Prncpal Component Transformaton. There exst some practcal lmts to applyng resoluton merge technques. If the resoluton rato of the two nput mages exceed a certan lmt, for example, SPOT panchromatc(0 0) and AVHRR magery(,00,00 m), t wll dffcult to produce a merged mage of any value. The Prncpal Component approach was selected as the resoluton mergng technque for ths research because t does not have the mergng mage band number lmts lke the IHS approach and t s more mathematcally rgorous than the Bravely Transform and the Multplcatce approaches. Prncpal Component assumptons usng the Landsat TM and SPOT panchromatc data resoluton merge were as follows. Fg. 3. The Method of Merge Process. After Merge Process, the mage classfcatons used both the back-propagaton of the neural networks and MLC of the supervsed classfcatons n ths study. 5. RESULTS AND DISCUSSION Authors study that area s used to demonstrate the classfyng land cover whch was nterpreted to produce the Anderson level I Land-used map. Table~2. Show the result of the classfcatons n ths paper. Typcally, compared wth classfcaton of the MLC n the unsupervsed classfcaton, Neural Network classfcaton s better than MLC. Yet the result of the Neural networks classfcaton at the Crop. Land s worse than MLC. It s not mportant to get precse classfcaton result because the am n ths paper s a classfcaton usng by the merged magery from SPOT and LANDSAT. Internatonal Archves of Photogrammetry and Remote Sensng. Vol. XXXIII, Part B7. Amsterdam

5 Evaluatons of the classfcatons were performed on an ndvdual and a comparatve bass wthn each classfcaton set. The ndvdual accuracy assessments were reported usng classfcaton error matrces. From these matrces, user's and producer's accuracy were calculated for all ndvdual classes as well as overall classfcaton accuracy. The Kappa statstc (KHAT) and ts varance were calculated for overall accuracy and for each category. In the Fg.4 5, there are much noses at the mergng mage of TM/SPOT than only TM usng the MLC of Supervsed classfcatons, but at the BP of Neural Networks noses has reduced the mergng mage than only TM. At the MLC the mergng mage reduces overall accuracy than only TM mage. Fg 4. MLC of Supervsed classfcatons Fg 5. B.P of Neural Networks classfcatons Table 3. Classfcaton Results Table 3. shows that the overall accuracy at the MLC of the Mergng Image was % wth a KHAT of and t at the MLC of only TM was %, whle a KHAT was But The overall accuracy at the B.P of the neural networks was 87.78% wth a KHAT of 0.84 and the TM was %, whle a KHAT was CONCLUSIONS 644 Internatonal Archves of Photogrammetry and Remote Sensng. Vol. XXXIII, Part B7. Amsterdam 2000.

6 Ths paper showed that almost any knd of neural network, usng a Standard Back-Propagaton Learnng algorthm for mnng the Mean Square error, s able to perform better than MLC. The reason s that the neural network s able to use a larger tranng set than the MLC. The results of the classfcatons usng the merged magery from SPOT and LANDSAT are as follows. The merged magery mproves the overall accuracy n the BP of Neural Network, yet the MLC dd not sgnfcantly mprove classfcaton results over the BP of Neural Network. It shows that the MLC wasn't affected by the merge magery. The BP of Neural Network provded the merged magery to better classfcaton accuracy. Fnally, authors wll expect hgher accuracy n addton to the texture, context and so on. REFERENCES Hepner, G.F., T.Longan, N.Rtter, and N.Bryant, 989. Artyfcal neural network classfcaton usng a mnmal tranng set: Comparson to conventonal supervsed classfcaton, Photogrammetrc Engneerng & Remote Sensng, 56(4): A.k shdmore, B.J Turner, W. Brnkhof, and E. Knowles, 997. Performancd of a Neural Network: Mappng Forests Usng GIS and Remotely Sensed Data, Photogrammetrc Engneerng & Remote Sensng, 63(5): John R. Jensen, 996. Introducetory Dgtal Image Processng, Prentce-Hall, pp Chrstopher M.Bshop, 995. Neural Networks for Pattern Recognton, Clarendon Press Oxford, pp Chn-Teng Ln and C.S. George Lee, 995. Neural Fuzzy Systems, Prentce-Hll, pp Carper, W.J., T.M. Lllesand, and R.W.Kefer, 990. The Use of Intensty-Hue-Saturaton Transformaton for Mergng SPOT Panchromatc and Multspectral Image Data, Photogrammetrc Engneer & Remote Sensng, 56(4), Cary T., 994. A World of Possbltes: Remote Sensng Data for your GIS, Geo Info Systems, September. Ehlers, M., 99. Mult-Sensor Image Funson Technques n Remote Sensng, ISPRS Journal of Photogrammetry and Remote Sensng, 46(3), pp9-30. Floyd F. Sabns, 997, Remote Sensng-Prncples and Interpretaton, W.H. freeman and Company. Congalton, R.G. 99, A Revew of Assessng the Accuracy of Calssfcatons of Remotely Sensed Data, Rempte sensng of Envronment, 37(), Internatonal Archves of Photogrammetry and Remote Sensng. Vol. XXXIII, Part B7. Amsterdam