请写一个卷积神经网络的程序来应用于遥感影像的云检测

好的，以下是一个简单的卷积神经网络程序，适用于遥感影像的云检测：

import tensorflow as tf

# 定义卷积神经网络模型
def cnn_model(images, batch_size, is_training):
    # 第一层卷积
    conv1 = tf.layers.conv2d(images, filters=32, kernel_size=[3, 3], padding='same', activation=tf.nn.relu)
    # 第一层池化
    pool1 = tf.layers.max_pooling2d(conv1, pool_size=[2, 2], strides=2)
    # 第二层卷积
    conv2 = tf.layers.conv2d(pool1, filters=64, kernel_size=[3, 3], padding='same', activation=tf.nn.relu)
    # 第二层池化
    pool2 = tf.layers.max_pooling2d(conv2, pool_size=[2, 2], strides=2)
    # 第三层卷积
    conv3 = tf.layers.conv2d(pool2, filters=128, kernel_size=[3, 3], padding='same', activation=tf.nn.relu)
    # 第三层池化
    pool3 = tf.layers.max_pooling2d(conv3, pool_size=[2, 2], strides=2)
    # 展开成一维向量
    flatten = tf.reshape(pool3, [batch_size, -1])
    # 全连接层
    fc = tf.layers.dense(flatten, units=128, activation=tf.nn.relu)
    # dropout层
    dropout = tf.layers.dropout(fc, rate=0.5, training=is_training)
    # 输出层
    logits = tf.layers.dense(dropout, units=2, activation=None)
    return logits

# 加载数据集
def load_data():
    # TODO：从本地或网络中加载遥感影像数据，并对其进行预处理
    pass

# 训练模型
def train():
    # 加载数据集
    train_data, test_data = load_data()
    # 构建模型
    images = tf.placeholder(tf.float32, [None, 256, 256, 3])
    labels = tf.placeholder(tf.float32, [None, 2])
    batch_size = 64
    is_training = tf.placeholder(tf.bool)
    logits = cnn_model(images, batch_size, is_training)
    # 定义损失函数和优化器
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))
    optimizer = tf.train.AdamOptimizer().minimize(loss)
    # 定义评估指标
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    # 创建session并进行训练
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(1000):
            batch_images, batch_labels = train_data.next_batch(batch_size)
            _, train_loss, train_acc = sess.run([optimizer, loss, accuracy], feed_dict={images: batch_images, labels: batch_labels, is_training: True})
            if (i+1) % 50 == 0:
                print("Step", i+1, "Loss", train_loss, "Accuracy", train_acc)
        # 在测试集上进行评估
        test_acc = 0.0
        for i in range(test_data.num_batches):
            batch_images, batch_labels = test_data.next_batch(batch_size)
            acc = sess.run(accuracy, feed_dict={images: batch_images, labels: batch_labels, is_training: False})
            test_acc += acc
        test_acc = test_acc / test_data.num_batches
        print("Test Accuracy", test_acc)

# 运行训练过程
train()

请注意，这个例子只是一个示例，针对具体的遥感影像数据集需要进行适当的修改和调整。同时，在实际应用中还需要考虑到数据增强、类别不平衡等问题。