A Method on Land Cover Classification by Combining Unsupervised Algorithm and Training Data

A Method on Land Cover Classification by Combining Unsupervised Algorithm and Training Data Chen Xiuwan Institute of Remote Sensing and GIS, Peking University Beijing 100871, China E-mail: xwchen@pku.edu.cn Hu Heping Department of Hydraulic Engineering Tsinghua University, Beijing 100084, China Ryutaro Tateishi Center for Environmental Remote Sensing (CEReS) Chiba University, Chiba 263, Japan Chung-Hyun Ahn Electric and Telecommunication Research Institute P. O. Box 1, Yusung-gu, Taejon 305-600, Korea Abstract In this paper, a method on land cover identifying by combining unsupervised algorithm and training data (CUT) was developed. The procedures of land cover classification by using the CUT method are: (a) to carry out remotely sensed image classification by using an unsupervised algorithm (e.g. ISODATA unsupervised classification) to make a land cover classification map, MAP 1, with n classes, where n is much greater than the proposed number of land cover classes, m, in the study area; (b) to collect training data for each of the proposed m classes; (c) to make a mask by using training data sets and statistically compute MAP 1 ; (d) to assign the class h in MAP 1 to class c in the final classification map, MAP 2, if and only if the number of pixels in class c is with the maximum ratio at the statistic. The CUT method was also used to produce a land cover classification map in a test area, Ansan City of Korea, with Thematic Mapper (TM) data acquired by Landsat-5. The accuracy analysis on the classification map, as compared to an unsupervised algorithm, showed that the CUT method is simple and reasonable. Introduction Land cover and land cover change are important elements of global environmental change processes (Dickinson, 1995; Hall et al., 1995), and the classification and change detection of land cover has great potential in remote sensing applications. A large body of research has been carried out by using various methodologies and algorithms to derive land cover and change information from different remotely sensed data (e.g., Bach et al, 1994; Carl and Roland, 1994; Chen, 1997; Lichtenegger, 1992; Sailer et al., 1997; Stolz and Wolfram, 1995; Tateishi and Kajiwara, 1991; Tateishi et al., 1991; Tateishi and Wen, 1996; Wismann, 1994). Traditional approaches to automated land cover mapping using remotely sensed data have employed pattern recognition techniques including both supervised and unsupervised approaches (Richards, 1992). More recently, techniques such as expert systems and neural networks have been used (Gong et al., 1996; Benediktsson et al., 1990; Wharton, 1989; Fried and Brodley, 1997). However, the complex component of terrestrial land cover makes it difficult to develop a general method for all applications in different regions in the world, even the best algorithms that have been developed are far from satisfactory given the requirements of land cover monitoring while using different remotely sensed data in different areas. Knowledge-based methodology has great potential for information extraction from remotely sensed data, but there still is much work to be done. In particular, the efforts by integrating multiple approaches, for example supervised and unsupervised algorithms, should be paid special attention. The accuracy of land cover mapping by using a supervised algorithm is only dependent on the accuracy and reasonableness of the training data collected and by using Geocarto International, Vol. 14, No. 4, December 1999 Published by Geocarto International Centre, G.P.O. Box 4122, Hong Kong. 15

unsupervised algorithm is only determined by the spectral data itself without any information about ground truth. The main purpose of the method on land cover identifying by combining unsupervised algorithm and training data (CUT) is to improve the accuracy of land cover classification made by unsupervised algorithm using ground truth data. Methodology By comprehensively making use of the spectral information acquired by the satellite sensor, ground truth information and training data, the CUT method integrates unsupervised clustering with ISODATA and error matrix analysis with training data. Land cover mapping by using CUT method starts from a clustering technique Using an unsupervised algorithm. The Iterative Self-Organizing Data Analysis Technique (ISODATA) was used to perform the classification from multispectal remotely sensed data. For example, a land cover classification map, MAP 1, with n classes was produced by ISODATA clustering, i.e. CLASS 1 = {C 1, C 2,..., C n } (1) and the proposed final classification map, MAP 2, is with m classes, where n is much greater than m. Training data in k spectral bands were collected for each of the proposed m classes, the training dataset for the ith class is DATA i = {D i1, D i2,..., D ij,...,d ik } (2) where i = 1,2,..., m j = 1,2,..., k the size of sample data for the ith class is P i (i = 1,2,..., m). To make a mask by using training data to produce m images IMAGE.MSK = {I 1.msk, I 2.msk,..., I i.msk,..., I m.msk} (3) where i = 1, 2,..., m. The next step is to overlay the image I i.msk with classification map, MAP 1, and perform a statistical analysis on MAP 1. Suppose the P i pixels on MAP 1 include r classes with the size of P i1, P i2,..., P ir, for class c 1, c 2,..., c r, respectively, where r < = m and r P i = Σ P ij. An index ID ij is calculated by normalizing P ij, j=1 ID ij = P ij /P i (4) Therefore, decide class h on MAP 1 is in class c on MAP 2 if, and only if, ID hc = MAX (ID c1, ID c2,..., ID ci,..., ID cm ) (5) where, i=1, 2,..., m. ISODATA Classification Algorithm Hundreds of clustering methods have been developed for land cover / use investigation in the field of remote sensing (Jensen, 1996). Clustering algorithms used for the unsupervised classification of remotely sensed data generally vary according to the efficiency with which the clustering takes place. Different criteria of efficiency lead to different approaches (Haralick and Fu, 1983). ISODATA is a widely used clustering algorithm (Tou and Gonzalez, 1977; Sabins, 1987; Jain, 1989). It represents a fairly comprehensive set of heuristic (rule-of thumb) procedures that have been incorporated into an iterative classification algorithm (ERDAS, 1994; USGS, 1990; Hayward, 1993). Many of the steps incorporated into the algorithm are a result of experience gabled through experimentation. ISODATA calculates class means which are evenly distributed in the data space and then iteratively clusters the remaining pixels using minimum distance techniques. Each iteration recalculates means and reclassifies pixels with respect to the new means. This process continues until the number of pixels in each class changes by less than the selected pixel change threshold or the maximum number of iterations is reached. Accuracy Assessment To correctly perform classification accuracy assessment, it is necessary to compare two sources of information: (1) the remote-sensing-derived classification map and (2) what we will call reference test information (which may in fact contain error). The relationship between these two sets of information is commonly summarized in an error matrix. The ideal situation to perform error evaluation is to locate reference test pixels in the study area. Most analysts prefer stratified random sampling by which a minimum number of samples are selected from each strata (i.e., land-use category). Some combination of random and stratified sampling provides the best balance between statistical validity and practical application (Dicks and Lo. 1990). Ideally, the x, y location of the reference test sites is determined using global positioning system (GPS) instruments (Abler, 1993). After the test reference information has been collected from the randomly located sites, it is compared on a pixelby-pixel basis with the information present in the remotesensing-derived classification map. Agreement and disagreement are summarized in the cells of the error matrix. By using simple descriptive statistical technique, overall accuracy is computed by dividing the total correct (sum of the major diagonal) by the total number of pixels in the matrix. Results and Discussion Test Area The study area, Ansan City, is located in the west coastal region of Korea (see Figure 1). The selected area is 324.0 km 2 (18 km x 18 km), which includes forest, grassland, urban and built-up land, lakes and reservoirs, sea and tidal zone, saltpan, agricultural land and wetland. 16

the Landsat TM data of May 20, 1993, one is subr 9305.img which includes seven TM bands, the other is r-ndvi93.img. which includes seven TM bands and Normalized Difference Vegetation Index (NDVI) band. Training Datasets Collecting In order to perform training data collection, it is necessary to make false color composite images. In reviewing the work of Chen, 1997, the best composite scheme for our purpose is: R:G:B = (0.7 * TM3 + 0.3 * TM6) : (0.5 * TM2 + 0.5 * TM4) : (0.3 * TM1 + 0.3 * TM5 + 0.4 * TM7) This was used to produce false color composite image, r93fcc.img. Two training datasets, 9305-1.roi and 9305-2.roi, were collected randomly from the false color composite image r93fcc.img. One of the two datasets was used to perform supervised classification while the other was used to assess the accuracy of classification maps. Figure 1 Location of the Study Area (Ansan City, Korea). Satellite Data The remotely sensed data were acquired by Thematic Mapper (TM) on Landsat 5 on May 20 1993, which covers a region cross Ansan, Incheon, and Seoul. This image consists of 7 bands by 2377 lines and 2357 samples and thus has a total of 5,602,589 points for each band. The data used in this study were extracted as a subscene from the original dataset, with 600 x 600 points which covers the study area shown in Figure 1. The statistical characteristics of TM data in the study area were shown in Table 1. Land Cover Classification System Certain classification schemes have been developed that can readily incorporate land-use and/or land-cover data obtained by interpreting remotely sensed data, e.g., U.S. Geological Survey Land Use/Land Cover Classification System (Anderson et al., 1976; USGS, 1990; Jensen, 1996), U.S. Fish & Wildlife Service Wetland Classification System (Cowardin et al., 1979; Jensen, 1996) and NOAA CoastWatch Land Cover Classification System (Dobson et al., 1995; Jensen, 1996). The U.S. Geological Survey Land Use/ Land Cover Classification System was chosen and referred to form the classification system for this study. By considering on the four levels of the U.S. Geological Survey Land Use/ Land Cover Classification System and the type of remotely sensed data typically used to provide the information, the classification system was created as in Table 2. Dataset Formation By image preprocessing including atmospheric and geometric corrections, two datasets were prepared based on Classifying For the comparable purpose, ISODATA unsupervised classification algorithm and CUT method were used to process the TM datasets subr9305.img and r-ndvi93.img. By ISODATA clustering, two classification maps, r-9.cla and rv-9.cla with 9 categories, were yielded. By using the CUT method, two classification maps, r-25.cla and rv- 25.cla with 25 classes, were made by ISODATA clustering. Then the training dataset 9305-1.roi was used to make masks and statistically compute the two maps, r-25.cla and rv-25.cla. Finally, the decision with the rule in equation (5) was applied to the final classification maps, thus cr-25.cla and crv-25.cla with 9 categories were produced. Accuracy Assessment The error matrices of the four classification maps r- 9.cla, rv-9.cla, cr-25.cla and crv-25.cla were analyzed by using the training data 9305-2.roi. As an example, Table 3 gives the error matrix of the classification map crv-25.cla, which shows that the map has high accuracy for identifying water area except the reservoir at the upper-right corner of the image, difficult to distinguish rangeland from forest land, saltpan from agricultural land, and urban and built-up land from barren land, and very difficult to distinguish wetland from tidal zone. Table 4 gives the overall accuracy calculated based on the error matrix analysis of each classification maps. For the two datasets, with and without NDVI, the classification accuracy is 0.68 and 0.82 by CUT method while it is 0.64 and 0.78 by ISODATA method. It is obvious that the accuracy of classification maps can be improved by using CUT method. Conclusion This study shows that the CUT method is a reasonable approach and useful tool to derive land cover information 17

Table 1 Statistic Characteristics of Landsat TM Data in Ansan, Korea Band Min Max Mean Stdev Eigenval 1 75 255 99.8212 11.1140 1914.2638 2 29 121 47.0792 7.1439 481.5700 3 29 183 55.0503 13.8503 73.9038 4 17 167 64.5812 22.3997 26.8180 5 8 255 75.7533 34.3531 15.4763 6 118 181 142.7686 6.6766 12.5563 7 0 255 36.3821 20.8036 1.0505 Covariance Matrix Band Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 1 123.520 73.521 133.785-55.047 139.981 36.456 137.614 2 73.521 51.035 96.163-5.948 128.315 24.644 105.462 3 133.785 96.163 191.832 0.546 270.933 50.310 215.246 4-55.047-5.948 0.546 501.745 515.677 36.857 208.216 5 139.981 128.315 270.933 515.677 1180.138 126.664 668.845 6 36.456 24.644 50.310 36.875 126.664 44.577 85.206 7 137.614 105.462 215.246 208.216 668.845 85.206 432.791 Correlation Matrix Band Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 1 1.000 0.926 0.869-0.221 0.367 0.491 0.595 2 0.926 1.000 0.972-0.037 0.523 0.517 0.710 3 0.869 0.972 1.000 0.002 0.569 0.544 0.747 4-0.221-0.037 0.002 1.000 0.670 0.246 0.447 5 0.367 0.523 0.569 0.670 1.000 0.552 0.936 6 0.491 0.517 0.544 0.246 0.552 1.000 0.613 7 0.595 0.710 0.747 0.447 0.936 0.613 1.000 Eigenvectors Band Band 1 Band 2 Band 3 Band 4 Band 5 Band 6 Band 7 1 0.105-0.416-0.365-0.067 0.680 0.398 0.241 2 0.094-0.236-0.285 0.118-0.033 0.095-0.911 3 0.195-0.441-0.494 0.238-0.587-0.121 0.328 4 0.349 0.709-0.590 0.014 0.120-0.109 0.022 5 0.780 0.035 0.391 0.075-0.153 0.456 0.012 6 0.089-0.079-0.121-0.957-0.229 0.040-0.044 7 0.451-0.258 0.162-0.064 0.319-0.772-0.038 Table 2 Land Cover Classification System in Ansan, Korea Table 3 Error Matrix of the Classification Map Derived from Landsat Data of Ansan, Korea (crv-25.cla) Class Description Unclassified 1 Forest land 2 Rangeland 3 Agricultural land 4 Wetland 5 Barren land 6 Urban and/or built-up land 7 Salt pan 8 Tidal zone 9 Water (sea and inland water body) Classification Forest Range- Agricultural Wet- Barren Urban and Salt Tidal Water Row land land land land land built-up land pan zone Total Forest land 5405 311 22 5738 Range land 206 385 87 14 692 Agricultural 4 2 1842 17 474 322 2661 Wetland 105 920 176 10 2729 6 3946 Barren land 1021 388 1409 Urban and 1 1 2 351 32 4766 5 5158 built-up land Salt pan 2203 973 620 400 4196 Tidal zone 1 154 67 8 16343 1 16574 Water 10 23 9362 9395 Column Total 5616 699 4153 1551 1053 5411 1475 19720 10091 49769 Overall Accuracy = 41017/49769 = 0.82 18

Method Table 4 from remotely sensed data, can provide estimates of land cover classification to an acceptable accuracy. It is undoubted that the CUT method can improve the accuracy of classification map made by ISODATA unsupervised algorithm. The accuracy of land cover classification in this study was influenced by the lack of ground truth information of the study area. With little information about the study area, it is difficult to collect accurate training data that is the key factor for classification. It is essential to collect in situ data by investigation. Ideally, the x, y location of the training sites is determined using global positioning system (GPS) instruments. Ancillary data, such as the maps of elevation, slope, aspect, geology, soils, hydrology, transportation network, boundaries, vegetation, etc., should be incorporated in the classification process to improve the accuracy and quality of remote-sensing-derived land-cover classification, if possible. 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