Systems Engineering and Electronics ›› 2025, Vol. 47 ›› Issue (5): 1432-1442.doi: 10.12305/j.issn.1001-506X.2025.05.06

• Electronic Technology • Previous Articles    

Effectiveness analysis and modeling of ship target detection utilizing hyperspectral remote sensing satellites

Wen SUN1, Juan CHENG2, Yihao WANG2,*, Kaichen CAO1, Mengmeng GE1, Liu HUANG1, Geng ZHANG2   

  1. 1. The 10th Research Institute of China Electronics Technology Group Corporation, Chengdu 610036, China
    2. Xi'an Institute of Optics and Precision Mechanics of CAS, Xi'an 710119, China
  • Received:2024-09-23 Online:2025-06-11 Published:2025-06-18
  • Contact: Yihao WANG

Abstract:

The effectiveness evaluation of hyperspectral satellites is an important part of hyperspectral remote sensing satellites development, holding significant practical implications for their design, development, and in-orbit effectiveness monitoring. Effectiveness analysis and modeling serve as the initial step in conducting an effectiveness evaluation of hyperspectral remote sensing satellites. To tackle this issue, firstly, typical spaceborne hyperspectral remote sensing imagers is introduced. Then, the effectiveness indicators for hyperspectral target detection is dissected and a deep analysis of the key factors is conducted that influence the effectiveness of hyperspectral satellite-based ship target detection. Meanwhile, the factors that impact the effectiveness of ship target detection are quantified. Taking the target detection probability as an example, a framework for the target detection effectiveness model is established. Subsequently, experimental analysis and model validation are performed using two publicly available ship target datasets, respectively, demonstrating the rationality and validity of the constructed model. This work holds significant importance for evaluating the effectiveness of hyperspectral satellite-based ship target detection.

Key words: remote sensing, hyperspectral satellite, ship detection, influencing factors, effectiveness evaluation

CLC Number: 

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