用pytorch 算法实现多源时空影像融合

多源时空影像融合可以使用深度学习算法来实现，其中常用的模型包括卷积神经网络（CNN）和循环神经网络（RNN）。在PyTorch中，可以使用PyTorch的torchvision和torch.nn模块来实现这些模型。

以下是一个简单的多源时空影像融合的PyTorch实现示例：

1. 导入必要的库

import torch
import torch.nn as nn
import torch.nn.functional as F

2. 定义卷积神经网络模型

class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=5, padding=2)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=5, padding=2)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 28 * 28, 512)
        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.pool1(F.relu(self.conv1(x)))
        x = self.pool2(F.relu(self.conv2(x)))
        x = self.pool3(F.relu(self.conv3(x)))
        x = x.view(-1, 64 * 28 * 28)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

3. 定义循环神经网络模型

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        combined = torch.cat((input, hidden), 1)
        hidden = self.i2h(combined)
        output = self.i2o(combined)
        output = self.softmax(output)
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, self.hidden_size)

4. 定义多源时空影像融合模型

class MultiSourceFusion(nn.Module):
    def __init__(self):
        super(MultiSourceFusion, self).__init__()
        self.conv_net = ConvNet()
        self.rnn = RNN(10, 20, 2)

    def forward(self, x1, x2):
        x1 = self.conv_net(x1)
        x2 = self.conv_net(x2)
        x = torch.cat((x1, x2), 1)
        output, hidden = self.rnn(x)
        return output

5. 训练模型

model = MultiSourceFusion()
criterion = nn.NLLLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)

for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs[0], inputs[1])
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()

        if i % 2000 == 1999:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

以上是一个简单的多源时空影像融合的PyTorch实现示例，你可以根据自己的数据和模型进行修改和调整。