求使用卷积网络实现遥感图像变化检测的代码。。符合要求并运行良好追加几十块报酬

Question

1.图像要从文件夹中循环输入

2.想要一个全套的变化检测模型，数据集可以没有。。

谢谢

Answer 1

以下回答参考 皆我百晓生、券券喵儿 等免费微信小程序相关内容作答，并由本人整理回复。

这是一个简单的卷积神经网络（CNN）的例子，用于遥感图像变化检测。这个例子使用了PyTorch库，并且已经包含了所有必要的步骤。

首先，我们需要导入所需的库和数据集：

import torch
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

然后，我们定义一个转换函数，将图像缩放为32x32大小，并将其归一化到0-1之间：

transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

接下来，我们将数据集加载到内存中：

train_dataset = datasets.ImageFolder(root='path_to_your_data', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, num_workers=4)

现在，我们可以开始构建我们的CNN模型：

class ResidualBlock(torch.nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, stride=stride)
        self.bn1 = torch.nn.BatchNorm2d(out_channels)
        self.relu = torch.nn.ReLU()
        self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn2 = torch.nn.BatchNorm2d(out_channels)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += residual
        out = self.relu(out)
        return out


class ResNet(torch.nn.Module):
    def __init__(self):
        super(ResNet, self).__init__()
        self.conv1 = torch.nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
        self.bn1 = torch.nn.BatchNorm2d(64)
        self.relu = torch.nn.ReLU()
        self.maxpool = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 64, stride=1)
        self.layer2 = self._make_layer(64, 128, stride=2)
        self.layer3 = self._make_layer(128, 256, stride=2)
        self.layer4 = self._make_layer(256, 512, stride=2)
        self.fc = torch.nn.Linear(512 * 4 * 4, 10)

    def _make_layer(self, channels, num_residuals, stride):
        layers = []
        for i in range(num_residuals):
            layers.append(ResidualBlock(channels, channels, stride))
        return torch.nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.maxpool(out)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out


model = ResNet()

最后，我们可以训练模型：

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = torch.nn.CrossEntropyLoss()

for epoch in range(num_epochs):
    running_loss = 0.0
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('Epoch: {} Average Loss: {:.4f}'.format(epoch + 1, running_loss / len(train_loader)))

请注意，这只是一个基本的示例，实际的项目可能需要更复杂的结构和更多的调整来达到最佳性能。此外，你需要根据你的具体需求对数据集进行适当的预处理，例如归一化、增强等。