With the rapid development of satellite remote sensing technology in China, it occupies an important position in the fields that require extremely high data accuracy, detail and in-depth information, and plays an increasingly important role in many aspects such as life, safety, and mineral resource detection. The application of imaging spectral images in various fields in China has just started. The purpose of this paper is to study the classification of ship images based on the structure of a distributed processing system for imaging spectral data. In this paper, referring to the feature estimation method and theory in traditional digital image processing system, a new technique of spectral image classification is proposed, and a new system is designed. Note that there are many similar or even duplicate group information in the image data, these feature information will waste a lot of time and computational resources in the process of repeated calculation, and cause the "Hughes" effect due to the increased participation in the amount of data in classification, accuracy will decrease. Therefore, it is necessary to select an appropriate area to participate in the calculation before distribution. To achieve the purpose of reducing time cost and improving accuracy and stability. In this paper, a selective-band distributed spectral data processing algorithm is proposed to solve the above performance and accuracy problems. Experiments show that the band selection algorithm in this paper consumes about one second less time than the traditional band non-selection algorithm, and the recognition accuracy rate reaches more than 97%.

Petkov Christopher. Ship Image Classification Based on the Architecture of Imaging Spectral Data Distributed Processing System. Distributed Processing System (2022), Vol. 3, Issue 1: 1-9. https://doi.org/10.38007/DPS.2022.030101.

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