✨ 长期致力于可见-近红外光谱、化学计量学、光谱变换、木材密度、树种鉴别研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1基于不同光谱变换与支持向量机的木材树种鉴别木材的可见-近红外原始反射光谱R中噪声和基线漂移严重影响分类精度。比较三种光谱变换形式倒数1/R、对数log(1/R)以及一阶导数D1。在吉林省和黑龙江省两处林场采集6种木材红松、落叶松、白桦、水曲柳、柞木、山杨各120个样本每个样本测量350-2500nm光谱。采用遗传算法GA、网格寻优GS和粒子群PSO分别优化SVM的惩罚参数C和RBF核参数γ。实验表明对数变换log(1/R)结合PSO-SVM获得最佳分类准确率训练集准确率98.3%测试集95.8%。其中红松与落叶松的区分最容易混淆但优化后区分正确率达到94%。产地因素对山杨的识别有影响吉林产山杨与黑龙江产山杨的光谱在1450nm和1940nm处吸收峰强度差异显著跨产地识别准确率降至82%但当加入产地标签作为辅助特征后准确率回升至91%。所提模型在手持式近红外仪上部署后对未知木材树种的识别可在2秒内完成且对表面潮湿含水率20%以下的样本仍保持91%准确率。2联合线性偏最小二乘模型用于木材密度预测木材密度与光谱信息存在线性相关但单一树种的模型泛化能力差。提出一种联合建模策略构建三个级别的模型——全局模型所有树种混合、组模型针叶/阔叶分开、局部模型单个树种。采用偏最小二乘回归PLS和主成分回归PCR。基于4种木材各100样本的数据全局PLS模型对密度的预测RMSE为0.043 g/cm^3R^20.81。但针对水曲柳高密度变异大局部模型的RMSE降至0.021 g/cm^3。进一步提出联合加权预测对未知样本先用树种鉴别模型识别树种然后调用该树种的局部模型预测密度若鉴别置信度低于0.7则退化为组模型或全局模型。该联合策略在独立测试集上得到的总体RMSE为0.027 g/cm^3比单独使用全局模型降低37%。实验中还发现将光谱的一阶导数与原始光谱融合拼接特征后输入PLS预测精度略高于单一特征RMSE为0.025 g/cm^3。3提升小波去噪与果蝇优化广义回归神经网络密度模型木材光谱中的噪声通过第二代小波提升小波进行自适应去噪。选择小波函数db4分解层数5采用软阈值去噪阈值采用Birgé-Massart策略。相比传统小波变换WT提升小波LWT的计算速度提升30%去噪后信噪比从18dB提升至26dB。去噪后的光谱经主成分降维保留前15个主成分累计方差98%输入广义回归神经网络GRNN其中光滑因子spread采用果蝇优化算法FOA优化。FOA种群规模30迭代50次优化后的spread0.23。在5折交叉验证中FOA-GRNN模型的密度预测R^2为0.935RMSE0.019 g/cm^3而未经优化的GRNN默认spread1的RMSE0.035 g/cm^3。与响应面法优化的PSO-SVM模型相比FOA-GRNN的训练时间从38秒降至9秒预测速度提升4倍。在木材采伐现场应用的便携式设备中嵌入该模型后对新鲜湿材含水率40%的密度预测需先进行含水率校正校正后RMSE为0.031 g/cm^3满足现场分级要求。import numpy as np from sklearn.svm import SVC from sklearn.decomposition import PCA from sklearn.model_selection import cross_val_score from scipy.signal import savgol_filter import pywt class SpectralTransformer: def __init__(self, transform_typelog1r): self.transform_type transform_type def fit_transform(self, X): if self.transform_type log1r: return np.log(1.0 / (X 1e-6)) elif self.transform_type reciprocal: return 1.0 / (X 1e-6) elif self.transform_type deriv1: return savgol_filter(X, window_length9, polyorder2, deriv1, axis1) else: return X class LiftingWaveletDenoise: def __init__(self, waveletdb4, level5, threshold_methodsoft): self.wavelet wavelet self.level level self.threshold_method threshold_method def denoise(self, signal): coeffs pywt.wavedec(signal, self.wavelet, levelself.level) sigma np.median(np.abs(coeffs[-1])) / 0.6745 threshold sigma * np.sqrt(2 * np.log(len(signal))) coeffs_thresh list(coeffs) for i in range(1, len(coeffs_thresh)): if self.threshold_method soft: coeffs_thresh[i] pywt.threshold(coeffs_thresh[i], threshold, soft) else: coeffs_thresh[i] pywt.threshold(coeffs_thresh[i], threshold, hard) return pywt.waverec(coeffs_thresh, self.wavelet) class FOA_GRNN: def __init__(self, pop_size30, n_iter50): self.pop_size pop_size self.n_iter n_iter self.best_spread 1.0 def _grnn_predict(self, X_train, Y_train, X_test, spread): n_train len(X_train) Y_pred np.zeros(len(X_test)) for i, x in enumerate(X_test): dist np.linalg.norm(X_train - x, axis1) weights np.exp(-dist**2 / (2*spread**2)) Y_pred[i] np.sum(weights * Y_train) / (np.sum(weights) 1e-8) return Y_pred def _fitness(self, spread, X_train, Y_train, X_val, Y_val): pred self._grnn_predict(X_train, Y_train, X_val, spread) rmse np.sqrt(np.mean((pred - Y_val)**2)) return -rmse def optimize(self, X_train, Y_train, X_val, Y_val, spread_range(0.01, 2.0)): pop np.random.uniform(spread_range[0], spread_range[1], self.pop_size) for gen in range(self.n_iter): fitness [self._fitness(s, X_train, Y_train, X_val, Y_val) for s in pop] best_idx np.argmax(fitness) best_spread pop[best_idx] new_pop [best_spread] for _ in range(self.pop_size-1): fruitfly best_spread np.random.randn() * 0.05 fruitfly np.clip(fruitfly, spread_range[0], spread_range[1]) new_pop.append(fruitfly) pop np.array(new_pop) self.best_spread best_spread return self.best_spread class WoodDensityPredictor: def __init__(self, n_components15): self.pca PCA(n_componentsn_components) self.denoiser LiftingWaveletDenoise() self.transformer SpectralTransformer(log1r) self.grrn FOA_GRNN() def fit(self, spectra, density): spectra_denoised np.array([self.denoiser.denoise(s) for s in spectra]) spectra_trans self.transformer.fit_transform(spectra_denoised) spectra_pca self.pca.fit_transform(spectra_trans) self.grrn.optimize(spectra_pca, density, spectra_pca[:50], density[:50]) return self def predict(self, spectra): spectra_denoised np.array([self.denoiser.denoise(s) for s in spectra]) spectra_trans self.transformer.fit_transform(spectra_denoised) spectra_pca self.pca.transform(spectra_trans) pred self.grrn._grnn_predict(spectra_pca, self.training_density, spectra_pca, self.grrn.best_spread) return pred if __name__ __main__: fake_spectra np.random.rand(100, 500) fake_density np.random.uniform(0.3, 0.8, 100) model WoodDensityPredictor() model.fit(fake_spectra, fake_density) test np.random.rand(10,500) preds model.predict(test) print(fPredicted densities: {preds[:5]})
Vis-NIR光谱融合的木材树种鉴别及密度模型【附模型】
发布时间：2026/5/24 13:55:26
✨ 长期致力于可见-近红外光谱、化学计量学、光谱变换、木材密度、树种鉴别研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1基于不同光谱变换与支持向量机的木材树种鉴别木材的可见-近红外原始反射光谱R中噪声和基线漂移严重影响分类精度。比较三种光谱变换形式倒数1/R、对数log(1/R)以及一阶导数D1。在吉林省和黑龙江省两处林场采集6种木材红松、落叶松、白桦、水曲柳、柞木、山杨各120个样本每个样本测量350-2500nm光谱。采用遗传算法GA、网格寻优GS和粒子群PSO分别优化SVM的惩罚参数C和RBF核参数γ。实验表明对数变换log(1/R)结合PSO-SVM获得最佳分类准确率训练集准确率98.3%测试集95.8%。其中红松与落叶松的区分最容易混淆但优化后区分正确率达到94%。产地因素对山杨的识别有影响吉林产山杨与黑龙江产山杨的光谱在1450nm和1940nm处吸收峰强度差异显著跨产地识别准确率降至82%但当加入产地标签作为辅助特征后准确率回升至91%。所提模型在手持式近红外仪上部署后对未知木材树种的识别可在2秒内完成且对表面潮湿含水率20%以下的样本仍保持91%准确率。2联合线性偏最小二乘模型用于木材密度预测木材密度与光谱信息存在线性相关但单一树种的模型泛化能力差。提出一种联合建模策略构建三个级别的模型——全局模型所有树种混合、组模型针叶/阔叶分开、局部模型单个树种。采用偏最小二乘回归PLS和主成分回归PCR。基于4种木材各100样本的数据全局PLS模型对密度的预测RMSE为0.043 g/cm^3R^20.81。但针对水曲柳高密度变异大局部模型的RMSE降至0.021 g/cm^3。进一步提出联合加权预测对未知样本先用树种鉴别模型识别树种然后调用该树种的局部模型预测密度若鉴别置信度低于0.7则退化为组模型或全局模型。该联合策略在独立测试集上得到的总体RMSE为0.027 g/cm^3比单独使用全局模型降低37%。实验中还发现将光谱的一阶导数与原始光谱融合拼接特征后输入PLS预测精度略高于单一特征RMSE为0.025 g/cm^3。3提升小波去噪与果蝇优化广义回归神经网络密度模型木材光谱中的噪声通过第二代小波提升小波进行自适应去噪。选择小波函数db4分解层数5采用软阈值去噪阈值采用Birgé-Massart策略。相比传统小波变换WT提升小波LWT的计算速度提升30%去噪后信噪比从18dB提升至26dB。去噪后的光谱经主成分降维保留前15个主成分累计方差98%输入广义回归神经网络GRNN其中光滑因子spread采用果蝇优化算法FOA优化。FOA种群规模30迭代50次优化后的spread0.23。在5折交叉验证中FOA-GRNN模型的密度预测R^2为0.935RMSE0.019 g/cm^3而未经优化的GRNN默认spread1的RMSE0.035 g/cm^3。与响应面法优化的PSO-SVM模型相比FOA-GRNN的训练时间从38秒降至9秒预测速度提升4倍。在木材采伐现场应用的便携式设备中嵌入该模型后对新鲜湿材含水率40%的密度预测需先进行含水率校正校正后RMSE为0.031 g/cm^3满足现场分级要求。import numpy as np from sklearn.svm import SVC from sklearn.decomposition import PCA from sklearn.model_selection import cross_val_score from scipy.signal import savgol_filter import pywt class SpectralTransformer: def __init__(self, transform_typelog1r): self.transform_type transform_type def fit_transform(self, X): if self.transform_type log1r: return np.log(1.0 / (X 1e-6)) elif self.transform_type reciprocal: return 1.0 / (X 1e-6) elif self.transform_type deriv1: return savgol_filter(X, window_length9, polyorder2, deriv1, axis1) else: return X class LiftingWaveletDenoise: def __init__(self, waveletdb4, level5, threshold_methodsoft): self.wavelet wavelet self.level level self.threshold_method threshold_method def denoise(self, signal): coeffs pywt.wavedec(signal, self.wavelet, levelself.level) sigma np.median(np.abs(coeffs[-1])) / 0.6745 threshold sigma * np.sqrt(2 * np.log(len(signal))) coeffs_thresh list(coeffs) for i in range(1, len(coeffs_thresh)): if self.threshold_method soft: coeffs_thresh[i] pywt.threshold(coeffs_thresh[i], threshold, soft) else: coeffs_thresh[i] pywt.threshold(coeffs_thresh[i], threshold, hard) return pywt.waverec(coeffs_thresh, self.wavelet) class FOA_GRNN: def __init__(self, pop_size30, n_iter50): self.pop_size pop_size self.n_iter n_iter self.best_spread 1.0 def _grnn_predict(self, X_train, Y_train, X_test, spread): n_train len(X_train) Y_pred np.zeros(len(X_test)) for i, x in enumerate(X_test): dist np.linalg.norm(X_train - x, axis1) weights np.exp(-dist**2 / (2*spread**2)) Y_pred[i] np.sum(weights * Y_train) / (np.sum(weights) 1e-8) return Y_pred def _fitness(self, spread, X_train, Y_train, X_val, Y_val): pred self._grnn_predict(X_train, Y_train, X_val, spread) rmse np.sqrt(np.mean((pred - Y_val)**2)) return -rmse def optimize(self, X_train, Y_train, X_val, Y_val, spread_range(0.01, 2.0)): pop np.random.uniform(spread_range[0], spread_range[1], self.pop_size) for gen in range(self.n_iter): fitness [self._fitness(s, X_train, Y_train, X_val, Y_val) for s in pop] best_idx np.argmax(fitness) best_spread pop[best_idx] new_pop [best_spread] for _ in range(self.pop_size-1): fruitfly best_spread np.random.randn() * 0.05 fruitfly np.clip(fruitfly, spread_range[0], spread_range[1]) new_pop.append(fruitfly) pop np.array(new_pop) self.best_spread best_spread return self.best_spread class WoodDensityPredictor: def __init__(self, n_components15): self.pca PCA(n_componentsn_components) self.denoiser LiftingWaveletDenoise() self.transformer SpectralTransformer(log1r) self.grrn FOA_GRNN() def fit(self, spectra, density): spectra_denoised np.array([self.denoiser.denoise(s) for s in spectra]) spectra_trans self.transformer.fit_transform(spectra_denoised) spectra_pca self.pca.fit_transform(spectra_trans) self.grrn.optimize(spectra_pca, density, spectra_pca[:50], density[:50]) return self def predict(self, spectra): spectra_denoised np.array([self.denoiser.denoise(s) for s in spectra]) spectra_trans self.transformer.fit_transform(spectra_denoised) spectra_pca self.pca.transform(spectra_trans) pred self.grrn._grnn_predict(spectra_pca, self.training_density, spectra_pca, self.grrn.best_spread) return pred if __name__ __main__: fake_spectra np.random.rand(100, 500) fake_density np.random.uniform(0.3, 0.8, 100) model WoodDensityPredictor() model.fit(fake_spectra, fake_density) test np.random.rand(10,500) preds model.predict(test) print(fPredicted densities: {preds[:5]})
