深度学习算法遥感影像分类python代码

深度学习算法用于遥感影像分类是一种广泛应用的方法，其原理是通过多层神经网络来学习特征并进行分类。以下是一个简单的基于Python的深度学习遥感影像分类代码示例：

1. 导入所需的Python库：

import numpy as np
import pandas as pd
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

2. 定义数据预处理函数：

def preprocess_data(train_dir, validation_dir, test_dir):
    train_datagen = ImageDataGenerator(rescale=1./255)
    validation_datagen = ImageDataGenerator(rescale=1./255)
    test_datagen = ImageDataGenerator(rescale=1./255)

    train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size=(128, 128),
        batch_size=32,
        class_mode='categorical'
    )

    validation_generator = validation_datagen.flow_from_directory(
        validation_dir,
        target_size=(128, 128),
        batch_size=32,
        class_mode='categorical'
    )

    test_generator = test_datagen.flow_from_directory(
        test_dir,
        target_size=(128, 128),
        batch_size=32,
        class_mode='categorical'
    )

    return train_generator, validation_generator, test_generator

3. 定义一个深度学习模型：

def create_model():
    model = Sequential()

    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)))
    model.add(MaxPooling2D((2, 2)))

    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))

    model.add(Conv2D(128, (3, 3), activation='relu'))
    model.add(MaxPooling2D((2, 2)))

    model.add(Flatten())

    model.add(Dense(128, activation='relu'))
    model.add(Dense(3, activation='softmax'))

    return model

4. 编译和训练模型：

def train_model(model, train_generator, validation_generator):
    model.compile(optimizer='adam',
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])

    model.fit_generator(
        train_generator,
        steps_per_epoch=100,
        epochs=10,
        validation_data=validation_generator,
        validation_steps=50
    )

5. 测试模型：

def test_model(model, test_generator):
    loss, accuracy = model.evaluate_generator(test_generator, steps=len(test_generator))
    print('Test Accuracy:', accuracy)

6. 主程序代码：

if __name__ == '__main__':
    train_dir = './data/train'
    validation_dir = './data/validation'
    test_dir = './data/test'

    train_generator, validation_generator, test_generator = preprocess_data(
        train_dir, validation_dir, test_dir)

    model = create_model()

    train_model(model, train_generator, validation_generator)

    test_model(model, test_generator)

这是一个简单的深度学习遥感影像分类的Python代码示例，你可以根据自己的需求和数据进行相应的修改和调整。