Because of the problem that the large amount of remote sensing data and the difficulty of feature selection lead to inaccurate land classification, we proposed a land classification algorithm based on attention u2net using hyperspectral technology. To solve the problem of a large amount of hyperspectral image data and high dimensionality, we adopted the LDA method for dimensionality reduction. To solve the problem that the traditional deep learning network does not focus enough on key areas, an attention u2net algorithm model is proposed, which uses an attention mechanism to strengthen the network’s learning on key areas to obtain better classification accuracy. We conducted experiments based on the existing three mainstream databases, and the results showed that the algorithm achieved an accuracy of 86.6% on the Indian Pines dataset, 95.2% on the Urban dataset, and 82.7% on the Fanglu dataset. Compared with other deep learning algorithms, the average improvement was 2.5%.
A Doppler asymmetric spatial heterodyne (DASH) interferometer was designed to measure atmospheric winds at a height of 60 to 80 km by observing the airglow emission line of molecular oxygen at 867 nm. The designed monolithic DASH interferometer exhibited decent thermal stability. The phase thermal drift of the fabricated interferometer obtained from thermal performance measurements was 0.376 rad / ° C. To accurately model and minimize the thermal drift performance of an interferometer in the design phase, it is necessary to include the influence of thermal distortion of the monolithic interferometer components. Therefore, an optical–structural–thermal integrated analysis method based on Zernike polynomials was proposed to accurately calculate the phase thermal drift of the interferometer. The optical model modified by the finite-element method calculated the phase thermal drift to be 0.420 rad / ° C, which agreed with the experimental result within 11.7%. This analysis method can accurately calculate and optimize thermal stability during the design of a DASH interferometer.
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