利用LeNet卷积神经网络对遥感影像进行分类处理代码实现

以下是一个基于LeNet卷积神经网络的遥感影像分类处理的代码实现，其中使用了Tensorflow框架：

import tensorflow as tf
import numpy as np
import os
from PIL import Image

# 数据路径
data_path = './data/'

# 训练集、验证集、测试集的比例
train_percent = 0.8
val_percent = 0.1
test_percent = 0.1

# 图像大小
image_width = 28
image_height = 28
image_channels = 3

# 分类数
num_classes = 6

# LeNet-5模型
def LeNet5(x, num_classes):
    # 第一个卷积层
    conv1 = tf.layers.conv2d(x, filters=6, kernel_size=[5, 5], strides=1, padding='same', activation=tf.nn.relu)
    # 第一个池化层
    pool1 = tf.layers.max_pooling2d(conv1, pool_size=[2, 2], strides=2)
    # 第二个卷积层
    conv2 = tf.layers.conv2d(pool1, filters=16, kernel_size=[5, 5], strides=1, padding='valid', activation=tf.nn.relu)
    # 第二个池化层
    pool2 = tf.layers.max_pooling2d(conv2, pool_size=[2, 2], strides=2)
    # 将特征图变成向量
    flatten = tf.reshape(pool2, shape=[-1, 5 * 5 * 16])
    # 第一个全连接层
    fc1 = tf.layers.dense(flatten, units=120, activation=tf.nn.relu)
    # 第二个全连接层
    fc2 = tf.layers.dense(fc1, units=84, activation=tf.nn.relu)
    # 输出层
    logits = tf.layers.dense(fc2, units=num_classes)
    return logits

# 加载数据
def load_data():
    x_data = []
    y_data = []
    for label, name in enumerate(os.listdir(data_path)):
        dir_path = os.path.join(data_path, name)
        for file_name in os.listdir(dir_path):
            file_path = os.path.join(dir_path, file_name)
            # 读取图像并缩放到指定大小
            image = Image.open(file_path).resize((image_width, image_height))
            # 将图像转换成numpy数组
            image_data = np.array(image)
            # 将图像的像素值归一化到0-1之间
            image_data = image_data / 255.0
            # 将图像和标签加入数据集
            x_data.append(image_data)
            y_data.append(label)
    # 将数据集转换成numpy数组
    x_data = np.array(x_data)
    y_data = np.array(y_data)
    # 将标签转换成one-hot编码
    y_data_one_hot = np.eye(num_classes)[y_data]
    # 将数据集打乱顺序
    perm = np.random.permutation(len(x_data))
    x_data = x_data[perm]
    y_data_one_hot = y_data_one_hot[perm]
    # 将数据集分成训练集、验证集、测试集
    train_size = int(train_percent * len(x_data))
    val_size = int(val_percent * len(x_data))
    test_size = len(x_data) - train_size - val_size
    x_train = x_data[:train_size]
    y_train = y_data_one_hot[:train_size]
    x_val = x_data[train_size:train_size+val_size]
    y_val = y_data_one_hot[train_size:train_size+val_size]
    x_test = x_data[-test_size:]
    y_test = y_data_one_hot[-test_size:]
    return x_train, y_train, x_val, y_val, x_test, y_test

# 训练模型
def train_model():
    # 加载数据
    x_train, y_train, x_val, y_val, x_test, y_test = load_data()
    # 创建占位符
    x = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels])
    y = tf.placeholder(tf.float32, [None, num_classes])
    # 创建模型
    logits = LeNet5(x, num_classes)
    # 定义损失函数和优化器
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(loss)
    # 定义评估指标
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    # 创建会话并开始训练
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch in range(10):
            for i in range(len(x_train) // 100):
                batch_x = x_train[i*100:(i+1)*100]
                batch_y = y_train[i*100:(i+1)*100]
                sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
            train_acc = sess.run(accuracy, feed_dict={x: x_train, y: y_train})
            val_acc = sess.run(accuracy, feed_dict={x: x_val, y: y_val})
            print('epoch %d, train accuracy %g, validation accuracy %g' % (epoch+1, train_acc, val_acc))
        test_acc = sess.run(accuracy, feed_dict={x: x_test, y: y_test})
        print('test accuracy %g' % test_acc)

if __name__ == '__main__':
    train_model()

这个代码实现了一个基于LeNet-5卷积神经网络的遥感影像分类处理，其中使用了一个包含6个类别的数据集。代码中使用了Tensorflow框架，并将数据集分成了训练集、验证集和测试集。训练过程中使用了Adam优化器和交叉熵损失函数，并在每个epoch结束时计算了训练集和验证集的准确率。最终在测试集上计算了模型的准确率。