The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Recently there have been many new developments in neural network (NN) research, and many new applications have been studied. It is well known that NN approaches have the ability to classify without assuming a distribution. We have proposed an NN model to combine the spectral and spacial information of a LANDSAT TM image. In this paper, we apply the NN approach with a normalization method to classify multi-temporal LANDSAT TM images in order to investigate the robustness of our approach. From our experiments, we have confirmed that our approach is more effective for the classification of multi-temporal data than the original NN approach and maximum likelihood approach.

A 0.4 µm stacked gate cell for a 64 Mbit flash memory has been developed which has the Symmetrical Side Wall Diffusion Self Aligned (SSW-DSA) structure. Using the proposed SSW-DSA cell with p+ pockets at both the drain and the source, and adequate punchthrough resistance to scale the gate length down to sub-half-micron has been obtained. It is also demonstrated that the channel erasing scheme applying negative bias to the gate, which is adopted for the SSW-DSA cell, shows lower trapped charges after Write/Erase (W/E) cycles evaluated by a charge pumping technique, and results in better endurance an retention characteristics than conventional erasing schemes.