基于卷积神经网络的遥感影像分类：技术综述与PyTorch实战

摘要：遥感影像分类是地理信息提取的核心任务之一。随着深度学习技术的飞速发展，卷积神经网络（CNN）凭借其强大的空间特征自动提取能力，逐渐取代传统机器学习方法，成为遥感分类领域的主流范式。本文系统梳理了CNN在遥感影像分类中的技术演进，涵盖基础架构（如LeNet、ResNet）、改进策略（空洞卷积、注意力机制）及典型应用场景（土地利用分类、目标检测）。同时，结合公开数据集（UC Merced、AID）给出分类精度对比表，并附完整代码示例（基于PyTorch），旨在为遥感与AI交叉领域的研究者提供一份兼具理论深度与工程实践的技术参考。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms, models
import os
from PIL import Image

# 1. 自定义数据集
class RemoteSensingDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.transform = transform
        self.classes = sorted(os.listdir(root_dir))
        self.class_to_idx = {cls: idx for idx, cls in enumerate(self.classes)}
        self.images = []
        for cls in self.classes:
            cls_path = os.path.join(root_dir, cls)
            for img_name in os.listdir(cls_path):
                self.images.append((os.path.join(cls_path, img_name), self.class_to_idx[cls]))
    
    def __len__(self):
        return len(self.images)
    
    def __getitem__(self, idx):
        img_path, label = self.images[idx]
        image = Image.open(img_path).convert('RGB')
        if self.transform:
            image = self.transform(image)
        return image, label

# 2. 数据预处理与加载
transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

train_dataset = RemoteSensingDataset('data/train', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)

# 3. 模型定义（使用预训练ResNet-18）
model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 21)  # 21类
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# 4. 训练配置
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)

# 5. 训练循环
num_epochs = 30
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0
    for inputs, labels in train_loader:
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item() * inputs.size(0)
        _, predicted = torch.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    
    epoch_loss = running_loss / len(train_dataset)
    epoch_acc = correct / total
    print(f'Epoch [{epoch+1}/{num_epochs}] Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
    scheduler.step()

print('训练完成！')

参考文献
[1] Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. NeurIPS.
[2] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. CVPR.
[3] Cheng, G., Han, J., & Lu, X. (2017). Remote sensing image scene classification: benchmark and state of the art. Proceedings of the IEEE.
[4] Tan, M., & Le, Q. V. (2019). EfficientNet: rethinking model scaling for convolutional neural networks. ICML.