用python写出基于深度学习SAR图像目标识别算法代码

这里提供一个简单的基于Keras实现的SAR图像分类代码，使用的CNN模型为VGG16：

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

# 定义VGG16模型
def VGG16():
    model = Sequential()
    model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=(128, 128, 1)))
    model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
    model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
    model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
    model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1, activation='sigmoid'))
    return model

# 加载数据
def load_data():
    train_data = np.load('train_data.npy')
    train_label = np.load('train_label.npy')
    test_data = np.load('test_data.npy')
    test_label = np.load('test_label.npy')
    return train_data, train_label, test_data, test_label

# 训练模型
def train_model(train_data, train_label, test_data, test_label):
    model = VGG16()
    model.compile(loss='binary_crossentropy',
                  optimizer='adam',
                  metrics=['accuracy'])
    model.fit(train_data, train_label,
              batch_size=32,
              epochs=10,
              verbose=1,
              validation_data=(test_data, test_label),
              shuffle=True)
    return model

# 测试模型
def test_model(model, test_data, test_label):
    score = model.evaluate(test_data, test_label, verbose=0)
    print('Test loss:', score[0])
    print('Test accuracy:', score[1])

if __name__ == '__main__':
    train_data, train_label, test_data, test_label = load_data()
    model = train_model(train_data, train_label, test_data, test_label)
    test_model(model, test_data, test_label)

需要注意的是，这里的数据应该是以NumPy数组形式保存的，可以通过`numpy.save()`和`numpy.load()`函数实现。数据应该是经过预处理的，例如进行归一化和零均值化等操作。在这个例子中，我们将SAR图像的大小统一调整为128x128，并将其转换为灰度图像（即单通道图像），所以输入数据的shape为(样本数, 128, 128, 1)。输出为二分类结果，所以输出数据的shape为(样本数, 1)。