Remote sensing image classification based on deep learning
Peng Yin
Henan Polytechnic University
DOI: https://doi.org/10.59429/esta.v13i1.13390
Keywords: remote sensing images; deep learning; design
Abstract
Remote sensing image classification is a core component of remote sensing technology applications, widely serving fields such as resource exploration and environmental monitoring. Traditional classification design schemes have limitations such as insufficient feature extraction and low classification accuracy, making it difficult to adapt to complex scene requirements. Deep learning, with its powerful ability to mine deep features, provides a new design concept for remote sensing image classification. This article focuses on the design of a remote sensing image classification system based on deep learning, reviews the relevant technical foundations, completes the design of each core module and overall architecture, solves the pain points of traditional design, and provides efficient and feasible design solutions for remote sensing image classification.
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