Land-Use Classification of Remotely Sensed Data Using Kohonen Self-organizing Feature Map Neural Networks

I The use of Kohonen Self-organizing Feature Map (KSOFM, or feature map) neural networks for land-use/land-cover classification from remotely sensed data is presented. Different from the traditional multi-layer neural networks, the KSOFM is a two-layer network that creates class representation by selforganizing the connection weights from the input patterns to the output layer. A test of the algorithm is conducted by classifying a Landsat Thematic Mapper (TM) scene for seven land-uselland-cover types, benchmarked with the maximumlikelihood method and the Back Propagation (BP) network. The network outpexformes the maximum-likelihood method for per-pixel classification when four spectral bands are used. A further increase in classification accuracy is achieved when neighborhood pixels are incorporated. A similar accuracy is obtained using the BP networks for classifications both with and without neighborhood information. The feature map network has the advantage of faster learning but has the drawback of being a slow classification process. Learning by the feature map is affected by a number of factors such as the network size, the codebooks partitioning, the available training samples, and the selection of the learning rate. The feature map size controls the accuracy at which class borders are formed, and a large map may be used to obtain accurate class representation. It is concluded that the feature map method is a viable alternative for land-use classification of remotely sensed data. Introduction Artificial neural networks have been widely studied for the land-use classification of remotely sensed data (e.g., Heermann and Khazenie, 1992; Bischof et al., 1992; Civco, 1993), and are now accepted alternatives to statistical classification techniques (Paola and Schowengerdt, 1997). The non-parametric neural network classifiers have numerous advantages over the statistical methods, such as no assumption about the probabilistic models of data, the ability to generalize in noisy environments, and the ability to learn complex patterns. Therefore, neural networks may perform well in cases where data are strongly non-Gaussian, such as classification that incorporates textural measures (e.g., Lee et al., 1990; Augusteijn et al., 1995), and multi-source data classification (e.g., Benediktsson et al., 1990; Gong, 1996; Bruzzone et al., 1997). This paper reports on the use of the Kohonen Self-OrganizI C.Y. Ji was with the Institute of Remote Sensing and GIS Applications, Peking University, Beijing 100871, China. He can presently be contacted c/o Xinning Jia, Asian Development Bank, P.O. Box 789, 0980 Manila, The Philippines (jiaxinninga adb.org). ing Feature Map (KSOFM) for land-uselland-cover classification. The algorithm is often described within the context of artificial neural networks in many textbooks. Basically, the feature map neural network is a vector quantizer which creates class representation onto a two-dimensional map by self-organizing the connection weights from a series of input patterns to outputs nodes. Figure 1 depicts the network configuration. Each node in the output layer is fully connected to its adjacent ones and to the input signals. The weight vectors are adjusted so that the density function of codebook clusters approximates the probability density function of the input vectors. This is referred to as topographic representation (Bose and Liang, 1996). The algorithm is biologically motivated, as maps of sensory surfaces are found in many parts of the brain (Kohonen, 1982). Applications of the algorithm are being made in many areas of pattern recognition tasks such as speech recognition (Kohonen, 1988), robotics (Graf and LaLonde, 1988), and image compression (Nasrabadi and Feng, 1988). A number of studies are also found in remote sensing classification. Orlando et al. (1990) used the method to classify four ground-cover classes (three types of sea-ice and one shadow class) from a radar image. They found that the KSOFM performed nearly as well as the multi-layer perceptron and the Gaussian classifiers when networks contained at least 20 nodes in either oneor two-dimensional configurations. A study by Hara etal. (1994) for cloud classification from SAR images concluded that the method yielded comparable results to that of Learning Vector Quantization and Migrating Means methods but had the advantages of classifying data with complex texture. Other applications of the Feature Map algorithm in remote sensing include feature selection (Iivarinen, 1994), and data preprocessing for neural network classification (Yoshida and Omatu, 1994). The current study is focused on the implementations of the algorithm such as the network design and training. A Landsat Thematic Mapper scene is used to classify seven land-use/ land-cover types. Classification using the feature map method is benchmarked with a the maximum-likelihood statistical classifier and the Back Propagation (BP) neural networks for both pixel and window classification (i.e., classification that uses the neighborhood pixels). All the image processing and classification work is carried out using an image processing system developed in the MS-Windows environment. Photogrammetric Engineering & Remote Sensing Vol. 66, No. 12, December 2000, pp. 1451-1460. 0099-111210016612-1451$3.0010 8 2000 American Society for Photogrammetry