面向水产养殖遥感的深度学习方法应用进展及展望OA

With the rapid expansion of aquaculture in China,there are increasing demands for improved site selection,monitoring,statistical analysis,and management of aquaculture areas.Remote sensing has become a critical tool in the statistical analysis of aquaculture.However,the extensive monitoring range of remote sensing images makes manual visual interpretation highly impractical due to its labor-intensive nature and high costs.Traditional computational methods modeling the spectral characteristics of targets and employing algorithms face several limitations,such as high sensitivity to environmental disturbances and poor robustness.In recent years,deep learning algorithms have emerged as a transformative approach for rapid and accurate analysis of remote sensing images,particularly in aquaculture.Deep learning models can simultaneously classify multiple targets and make more comprehensive use of information derived from various sensors embedded in images,enhancing the accuracy and robustness of image recognition.The adaptive learning capabilities of deep learning models allow them to generalize across diverse environmental conditions and overcome the limitations of traditional algorithms.This paper systematically reviews the applications of deep learning algorithms in fisheries and aquaculture remote sensing.It introduces essential processes such as remote sensing image acquisition,radiometric and geometric correction,and the establishment of datasets tailored to specific aquaculture scenarios.The discussion also covers the selection and implementation of various deep learning algorithms,including model training and evaluation metrics.In the second part,the paper delves into the practical application processes of deep learning methods in aquaculture remote sensing.Key areas of focus include site selection and classification of aquaculture zones,estimation of aquaculture area and economic output,and monitoring of inter-annual variations in aquaculture.The integration of deep learning in these processes not only improves the efficiency of aquaculture management but also provides more accurate and reliable data for decision-making.Finally,the paper summarizes the current status and challenges of deep learning algorithms in the aforementioned applications,identifying gaps and areas for improvement.The discussion emphasizes the need for continued innovation and refinement of deep learning models to enhance their applicability and performance in aquaculture remote sensing.Recommendations are provided for future research directions,including the integration of multi-sensor data,the development of more robust models that can adapt to varying environmental conditions,and the exploration of real-time processing capabilities.The paper concludes with a forward-looking perspective on the potential of deep learning algorithms to revolutionize aquaculture monitoring and management,contributing to more sustainable and efficient practices in the industry.