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利用深度学习和无监督特征提取进行珊瑚物种分类的新方法

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL
Journal of Spatial Science Pub Date : 2024-08-14 DOI:10.1080/14498596.2024.2383881
R. Jannathul Firdous, S. Sabena
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引用次数: 0

摘要

本研究提出了一种新颖的方法，通过整合三卷积深度自动编码器（TRI-CDAE）和稀疏深度自动编码器（SPDA...
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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A novel approach to coral species classification using deep learning and unsupervised feature extraction
This study presents a novel methodology to enhance coral species classification in underwater images by integrating Tri Convolutional Deep Autoencoders (TRI-CDAE) and Sparse Deep Autoencoders (SPDA...
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来源期刊
Journal of Spatial Science
Journal of Spatial Science 地学-地质学
CiteScore
5.00
自引率
5.30%
发文量
25
审稿时长
>12 weeks
期刊介绍： The Journal of Spatial Science publishes papers broadly across the spatial sciences including such areas as cartography, geodesy, geographic information science, hydrography, digital image analysis and photogrammetry, remote sensing, surveying and related areas. Two types of papers are published by he journal: Research Papers and Professional Papers. Research Papers (including reviews) are peer-reviewed and must meet a minimum standard of making a contribution to the knowledge base of an area of the spatial sciences. This can be achieved through the empirical or theoretical contribution to knowledge that produces significant new outcomes. It is anticipated that Professional Papers will be written by industry practitioners. Professional Papers describe innovative aspects of professional practise and applications that advance the development of the spatial industry.
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