高光谱和深度学习结合检测SSC相关的代码

高光谱成像（Hyperspectral Imaging, HS）结合深度学习（Deep Learning, DL）用于土壤沙粒结构（Soil Sand Structure, SSC）检测的代码示例，通常是用Python和一些深度学习库如TensorFlow或PyTorch编写的。这里提供一个简化的概念：

# 导入库
import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.utils import to_categorical

# 假设我们有一个高光谱数据集X_spectra和对应的标签y_ssc
X_train, X_test, y_train, y_test = train_test_split(X_spectra, y_ssc, test_size=0.2)

# 数据预处理，标准化
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 将标签转换为one-hot编码
y_train = to_categorical(y_train, num_classes=len(np.unique(y_ssc)))
y_test = to_categorical(y_test, num_classes=len(np.unique(y_ssc)))

# 定义卷积神经网络模型
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(spectral_depth, spatial_size, 1)),
    MaxPooling2D(pool_size=(2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(pool_size=(2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(len(np.unique(y_ssc)), activation='softmax')
])

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(X_train.reshape(-1, spectral_depth, spatial_size, 1), y_train, epochs=50, validation_data=(X_test.reshape(-1, spectral_depth, spatial_size, 1), y_test))

# 测试模型性能
score = model.evaluate(X_test.reshape(-1, spectral_depth, spatial_size, 1), y_test, verbose=0)
print("Test accuracy:", score[1])

这个例子是一个基本的流程，实际应用中可能还需要更多的数据预处理步骤以及调整模型结构以获得最佳结果。