Python keras代码：卷积神经网络对遥感影像的变化检测

以下是使用Keras实现的卷积神经网络对遥感影像的变化检测的代码：

import numpy as np
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
from keras.optimizers import Adam

# 加载数据
X_train = np.load('X_train.npy')
y_train = np.load('y_train.npy')
X_test = np.load('X_test.npy')
y_test = np.load('y_test.npy')

# 构建模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', padding='same', input_shape=X_train.shape[1:]))
model.add(Conv2D(32, kernel_size=(3,3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same'))
model.add(Conv2D(64, kernel_size=(3,3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

# 编译模型
model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.0001), metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test))

# 评估模型
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

这段代码中，我们使用了Keras的Sequential模型来构建卷积神经网络，其中包括了多个Conv2D层、MaxPooling2D层、Dropout层和Dense层。我们使用Adam优化器和二元交叉熵损失函数来编译模型，并使用训练集进行训练。最后，我们使用测试集对模型进行评估，输出测试集上的损失和准确率。