用深度神经网络提升作物图谱识别准确率，对比三种算法效果。
Machine Learning Approaches on Crop Pattern Recognition a Comparative Analysis
- 采用深度神经网络进行作物模式分类，替代传统方法。
- 相比朴素贝叶斯和随机森林，新模型识别准确率显著提升。
- 适合农业遥感监测与智能决策系统研究者参考。
监测农业活动对保障粮食安全至关重要。遥感技术在大范围连续监测种植活动方面发挥重要作用。利用时间序列遥感数据生成作物图谱，通过分类算法识别作物类型并绘制耕地利用图。传统方法如支持向量机（SVM）和决策树已被应用，但本文提出基于深度神经网络（DNN）的分类方法，以提升作物图谱识别性能，并与朴素贝叶斯（Naive Bayes）和随机森林（Random Forest）两种机器学习方法进行对比分析。
原文摘要 · Abstract (English)
Monitoring agricultural activities is important to ensure food security. Remote sensing plays a significant role for large-scale continuous monitoring of cultivation activities. Time series remote sensing data were used for the generation of the cropping pattern. Classification algorithms are used to classify crop patterns and mapped agriculture land used. Some conventional classification methods including support vector machine (SVM) and decision trees were applied for crop pattern recognition. However, in this paper, we are proposing Deep Neural Network (DNN) based classification to improve the performance of crop pattern recognition and make a comparative analysis with two (2) other machine learning approaches including Naive Bayes and Random Forest.
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