Remote sensing image segmentation using a Kalman filter-trained neural network

✍ Scribed by K. S. Chen; D. H. Tsay; W. P. Huang; Y. C. Tzeng

• Publisher: John Wiley and Sons
• Year: 1996
• Tongue: English
• Weight: 774 KB
• Volume: 7
• Category: Article
• ISSN: 0899-9457
• DOI: 10.1002/(sici)1098-1098(199622)7:2<141::aid-ima11>3.0.co;2-4

✦ Synopsis

This article describes the application of a neural network to the segmentation of remote sensing images of multispectral SPOT and fully polarimetric SAR data. The structure of the network is a modified multilayer perceptron and is trained by the Kalman filter theory. The internal activity of the network is a nonlinear function, while the function at output layer is linearized through the use of a polynomial basis function, thus allowing us employ the theory of Kalman filtering as the learning rule. The network is therefore called the dynamic learning (DL) neural network. It is found that, when applied to SPOT and SAR data, the DL neural network gives a good segmentation results, while the learning rate is very promising compared to the standard backpropagation network and other fast-learning networks. In particular, for polarimetric SAR data, optimum polarizations for discriminating between different terrains are automatically built in through the use of the Kalman filter technique. The suitability and effectiveness of the proposed DL neural network to the segmentation of remote sensing images is demonstrated. 0 1996 John Wiley & Sons, Inc.