Neural Approah to Remote Sensing Image classifiation
Kozo Okazaki, Hisashi Taketani, Yutaka Fukui
Faculty of Engineering, Tottori University
Hiroshi Mitsumoto
Faculty of Engineering Science, Osaka University
Shinichi Tamura
Faculty of Medicine, Osaka University
Takashi Hoshi
Institute of Information Sciences and Electronics,
University of Tsukuba
Kiyoshi Torji
Faculty of Agriculture, Kyoto University
Masami Iwasaki
Faculty of Agriculture, Tottori University
Abstract
A neural network approach to the remote sensing image data is proposed. Multi-channel image he composed of neighbouring pixels are used as input to the back propagation network. The training in done by error-back-propagation algorithm. We use a 32 bit personal computer (NEC PC-9801 RA), bjper frame board memory and ImPP board which is used as the neuro-accelerator. The window are of 10X10 pixels with ground truth such as mountain, coast and sea etc. are used as the training data. Each neuron of the output layer correspond to each category respectively.
Introduction
In recent years, researches of feature extraction and classification using neural networks flousrish remarkably. Especially, back propagation method (BP) is being tried widely because of the simplexes of training and calculation algorithms and the excellent ability of learning [eg. (1)]. The classification of multi-specteral remote seining image is, usually, based on multi-variable analysis. This method examines the statistical characteristics for every pixels. The mage shows a market trend of being classified excessively. Looking for the land are in these cases, we need to recombine the clusters. On the other hand, classifications of the ample flat are including sea, lake area, etc. need to use more large regions for processing. This paper deals with the classification by neural networks based on 10 X 10 pixel square regions.
Error back propagation
Simpleness of the calculation algorithm and excellent ability of learning, BP is widely and actively used for many fields.
Fig. 1 (a) shows a schematic diagram of neural networks for multi-input, multi-output in
Fig.1 (a) and Fig.1 (b) is a input-output relation of a neuron unit.
We explain the signal flow of Fig. 1 (a)
Fig. 1(a) Schematic diagram of neural networks |
Fig. 1(b) Input-output function of a neuron unit |
Fig.1 Error back propagation
here,
m: the number of layer,
O
ki: output of the I-the unit of the k-the layer,
i
kj: input sum of the j-the unit of the k-th layer,
x
i: input pattern,
y
i: teaching pattern,
w
k-1ki j weight between the j-th unit of the (k-1)-the layer and the j-th unit of the k-th layer.
f: input-output function of the unit; f is the same function for all the units.
f (x) = ½ (1 + tanh (X/uo))
Error back propagation method (BP) is based on the minimization of square error of the output and the teaching signal by changing the weight w.
The algorithm is given by
