Various types of optical materials and devices used in special environments must satisfy durability and optical properties. In order to improve the durability of zinc sulfide multispectral (MS ZnS) substrates with transmission wavelengths from visible to infrared, Ge-Sb-Se-based chalcogenide glass was used as a sealing material to bond the MS ZnS substrates. Wetting tests of the Ge-Sb-Se-based chalcogenide glass were conducted to analyze flowability as a function of temperature, by considering the glass transition temperature (Tg) and softening temperature (Ts). In the wetting test, the viscous flow of the chalcogenide glass sample was analyzed according to the temperature. After placing the chalcogenide glass disk between MS ZnS substrates (20 × 30 mm), the sealing test was performed at a temperature of 485 °C for 60 min. Notably, it was found that the Ge-Sb-Se-based chalcogenide glass sealed the MS ZnS substrates well. After the MS ZnS substrates were sealed with chalcogenide glass, they showed a transmission of 55 % over 3~12 μm. The tensile strength of the sealed MS ZnS substrates with Ge-Sb-Se-based chalcogenide glass was analyzed by applying a maximum load of about 240 N, confirming its suitability as a sealing material in the far infrared range.

In general, neural networks are widely used for the category classification of multispectral images. Since the input multispectral images into neural networks we, however, low contrast images, neural networks converge very slowly and are of bad performance. To overcome this problem, we propose a new image enhancement method which consists of smoothing process, finding the main valley and enhancement process. In addition the enhanced images by the proposed method are used as the input of neural networks for the category classification. When the new category classification method is applied to multispectral LANDSAT TM images, we verified that the neural networks converge very lastly and that the overall category classification performance is improved.