OPTIMIZATION OF BP NEURAL NETWORK FOR CHLOROPHYLL-A CONCENTRATION INVERSION BASED ON SPARROW SEARCH ALGORITHM: A CASE STUDY OF XIAOJIANG
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摘要:
针对传统反向传播(Back Propagation, BP)神经网络在叶绿素a浓度反演中对初始值敏感、容易陷入局部最优的问题, 文章提出基于麻雀搜索算法(Sparrow Search Algorithm, SSA)优化SSA-BP反演模型。结合大疆RTK300无人机搭载AFX-10高光谱相机的遥感数据与小江回水区同步地面采样数据, 构建新型反演模型。结果显示: (1)Savitzky-Golay(SG)平滑显著优化光谱数据质量, 使SSA-BP模型决定系数(R2)提升至0.98; (2)相较于传统BP神经网络, SSA-BP模型反演精度全面提升, 其中渠马水域平均绝对误差(MAE)降低了59.14%, 均方根误差(RMSEP)降低了60.78%, 相对百分比差异(RPD)提高了57.32%; (3)SSA-BP模型克服了传统BP模型在低浓度区域(R2从0.94降至0.76)的性能衰减, 在不同叶绿素a浓度梯度下均保持稳定高精度, R2最高达到0.98。研究证实SSA-BP模型显著提升无人机高光谱遥感反演叶绿素a的精度与适应性, 为内陆水体生态环境监测提供可靠技术手段。
Abstract:Chlorophyll-a concentration is a crucial parameter characterizing water ecological environment quality. To address the issues of traditional Back Propagation (BP) neural networks, which are highly sensitive to initial values and tendency to local optima in chlorophyll-a inversion, this study proposes an SSA-BP inversion model optimized by using the Sparrow Search Algorithm (SSA). A novel inversion model was constructed by integrating remote sensing data from the DJI RTK300 UAV equipped with an AFX-10hyperspectral camera and synchronous ground sampling data from the Xiaojiang backwater area. The results demonstrate that: (1) The application of Savitzky-Golay (SG) smoothing significantly improved spectral data quality, increasing the determination coefficient (R2) of the SSA-BP model to 0.98; (2) Compared with traditional BP neural networks, the SSA-BP model showed comprehensive improvement in inversion accuracy, with the Quma water area exhibiting a 59.14% reduction in Mean Absolute Error (MAE), 60.78% decrease in Root Mean Square Error of Prediction (RMSEP), and 57.32% increase in Relative Percent Difference (RPD); (3) The SSA-BP model overcame the performance degradation of traditional BP models in low-concentration regions (where R2 decreased from 0.94 to 0.76), maintaining stable high precision across different chlorophyll-a concentration gradients, with the highest R2 reaching 0.98. This research confirms that the SSA-BP model significantly enhances the accuracy and adaptability of UAV hyperspectral remote sensing in chlorophyll-a inversion, providing a reliable technical approach for ecological environment monitoring in inland water bodies.
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图 1 小江回水区及其监测点分布图
Figure 1. Distribution of Xiaojiang backwater area and its monitoring points
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图 3 叶绿素a浓度与光谱反射率值的相关系数曲线
Figure 3. Correlation coefficient curve between chlorophyll concentration and spectral reflectance value
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图 4 采样点光谱反射率曲线及预处理结果
a. 原光谱曲线; b. 一阶导数; c. 数据中心化; d. 多元散射校正; e. SG平滑; f. 标准正态变换
Figure 4. Spectral reflectance curves of sampling points and pretreatment results
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图 6 渠马、高阳和黄石的BP与SSA-BP模型的预测值与实测值对比
Figure 6. Comparison of predicted and measured values of BP and SSA-BP models for QM, GY and HS
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图 7 八个监测区域叶绿素a浓度遥感反演
Figure 7. Remote sensing inversion of chlorophyll a concentration in 8 key monitoring areas
表 1 BP和SSA-BP模型及光谱预处理方法比较
Table 1 Comparison of BP and SSA-BP models and spectral pretreatment methods
区域
Region模型
Model评价指标
Evaluation Index预处理方法Pretreatment Method 归一化 D1 MCX MSC SG SNV MAE 2.3572 3.0845 1.7763 2.106 1.3199 1.8712 RMSEP 2.4932 4.3058 2.1856 2.6287 1.5979 2.6898 SSA-BP R2 0.95514 0.82758 0.96742 0.94652 0.97487 0.95473 RPD 4.7254 2.4104 6.144 4.3788 6.3136 4.704 MAPE 0.053826 0.06776 0.03602 0.04388 0.03254 0.04375 QM MAE 2.1446 7.8347 3.0951 3.0401 3.2305 5.7849 RMSEP 2.8452 8.9763 3.6434 4.1275 4.0741 6.7402 BP R2 0.90973 0.67639 0.73801 0.91032 0.93604 0.72059 RPD 3.3422 1.8805 1.9604 4.3334 4.0132 1.952 MAPE 0.043334 0.21282 0.06716 0.07729 0.13373 0.12939 MAE 1.9676 2.954 2.4532 1.9519 1.4026 2.1022 RMSEP 2.4408 3.4461 3.2868 2.2003 2.051 2.8688 SSA-BP R2 0.92249 0.62817 0.84753 0.90903 0.94879 0.90369 RPD 4.0574 1.6399 2.7661 3.3235 4.5566 3.276 MAPE 0.10975 0.25805 0.20731 0.16217 0.12099 0.12754 GY MAE 2.864 4.1071 2.5727 2.9092 1.6346 2.5189 RMSEP 3.7608 4.9562 2.9529 3.8957 2.3909 3.1298 BP R2 0.83707 0.62619 0.85016 0.79983 0.91853 0.85359 RPD 2.4909 1.6429 2.5866 2.3262 3.5046 2.903 MAPE 0.22711 0.29904 0.17983 0.20639 0.13894 0.28308 MAE 0.53218 1.065 0.68832 0.39329 0.3053 0.4573 RMSEP 0.59886 1.3948 0.87756 0.45195 0.39288 0.53745 SSA-BP R2 0.88499 0.53494 0.77597 0.91034 0.96852 0.83141 RPD 2.9526 1.4945 2.3422 3.7329 6.227 2.4378 MAPE 0.061797 0.12891 0.08751 0.04979 0.03456 0.04939 HS MAE 0.57029 1.2976 0.57965 0.86522 0.58747 0.91092 RMSEP 0.75499 1.4863 0.81558 1.099 0.83065 1.1727 BP R2 0.72308 0.4345 0.77105 0.72215 0.76364 0.7368 RPD 2.0086 1.3976 2.0904 2.8485 2.0589 2.5097 MAPE 0.069112 0.16383 0.06806 0.08869 0.07557 0.09026 
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