激光与光电子学进展, 2019, 56 (15): 153002, 网络出版: 2019-08-05 复制并引用该论文  本文已被引 4 次  被引通知

图 & 表

图 1. 春小麦冠层光谱曲线。(a)不同含水量春小麦冠层光谱曲线;(b) 0~2阶微分春小麦冠层光谱曲线

Fig. 1. Canopy spectral curves of spring wheat. (a) Canopy spectral curves of spring wheat with different water contents; (b) canopy spectral curves of spring wheat with 0-order to 2-order differentials

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图 2. MLWC和光谱反射率的相关系数。(a) MLWC和0阶光谱的相关系数;(b) MLWC和0.2、0.4、0.6、0.8阶光谱的相关系数;(c) MLWC和1阶光谱的相关系数;(d) MLWC和1.2阶光谱的相关系数;(e) MLWC和1.4阶光谱的相关系数;(f) MLWC和1.6阶光谱的相关系数;(g) MLWC和1.8阶光谱的相关系数;(h) MLWC和2阶光谱的相关系数

Fig. 2. Correlation coef?cients between MLWC and spectral re?ectance. (a) Correlation coefficients between MLWC and 0-order spectrum; (b) correlation coefficients between MLWC and 0.2, 0.4, 0.6, 0.8-order spectra; (c) correlation coefficients between MLWC and 1-order spectrum; (d) correlation coefficients between MLWC and 1.2-order spectrum; (e) correlation coefficients between MLWC and 1.4-order spectrum; (f) correlation coefficients between MLWC and 1.6-order spectrum; (g) correlation coefficients bet

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图 3. BP神经网络模型实测值与预测值的拟合分析结果。(a) 1阶微分;(b) 1.2阶微分;(c) 1.4阶微分;(d) 1.6阶微分;(e) 1.8阶微分;(f) 2阶微分

Fig. 3. Fitting analysis results between measured values and predicted values by BP neural network model. (a) 1-order differential; (b) 1.2-order differential; (c) 1.4-order differential; (d) 1.6-order differential; (e) 1.8-order differential; (f) 2-order differentials

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表 1SPA筛选出的波段个数及组合

Table1. Numbers and combinations of bands selected by SPA

Differentialorder Numberof bands Band combinationsselected by SPA/nm 0 0 — 0.2 0 — 0.4 0 — 0.6 0 — 0.8 0 — 1 2 867、2089 1.2 13 420,486,616,690,912,957,1223,1660,1978,2103,2111,2238,2276 1.4 4 859,888,1255,1953 1.6 5 420,1278,1627,2238,2286 1.8 6 823,875,892,1004,1627,2286 2 5 888,911,1144,1686,2286

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表 2建模结果比较

Table2. Comparison of modeling results

Differentialorder Optimal BPneuralnetworkstructure θC RC2 θV RV2 δ 1 2-4-1 0.692 0.737 0.496 0.628 1.395 1.2 13-12-1 1.008 0.472 0.366 0.789 2.050 1.4 4-5-1 1.174 0.482 0.417 0.809 1.943 1.6 5-9-1 0.553 0.828 0.634 0.807 1.495 1.8 6-4-1 0.701 0.751 0.227 0.917 3.253 2 5-8-1 0.733 0.698 0.308 0.840 2.298

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吾木提·艾山江, 买买提·沙吾提, 马春玥. 基于分数阶微分和连续投影算法-反向传播神经网络的小麦叶片含水量高光谱估算[J]. 激光与光电子学进展, 2019, 56(15): 153002. Hasan Umut, Sawut Mamat, Chunyue Ma. Hyperspectral Estimation of Wheat Leaf Water Content Using Fractional Differentials and Successive Projection Algorithm-Back Propagation Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(15): 153002.