Land-use classification of remotely sensed data using kohonen self-organizing feature map neural networks

摘要

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 self-organizing 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-use/land-cover types, benchmarked with the maximum- likelihood method and the Back Propagation (BP) network. The network outperformes 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.