AI image processing algorithms for remote sensing change detection leverage deep learning and machine learning techniques to identify differences between multi-temporal satellite or aerial images. Here are key algorithms with explanations and examples:
1. Convolutional Neural Networks (CNNs)
- Explanation: CNNs extract spatial features from images. For change detection, a Siamese CNN architecture is commonly used, where two identical networks process input images (e.g., from different time points) and compare their feature maps.
- Example: A Siamese CNN takes two Landsat images (pre- and post-event) and outputs a change map highlighting altered areas, such as deforestation or urban expansion.
2. Recurrent Neural Networks (RNNs) / Long Short-Term Memory (LSTM)
- Explanation: RNNs and LSTMs capture temporal dependencies in sequential remote sensing data. They are useful for detecting gradual changes over time.
- Example: LSTM networks analyze a time series of Sentinel-1 SAR images to monitor crop growth cycles or water body variations.
3. U-Net and Its Variants
- Explanation: U-Net, originally designed for medical imaging, is widely adapted for change detection due to its encoder-decoder structure, which preserves spatial details while upsampling features.
- Example: A modified U-Net processes paired high-resolution images (e.g., from GF-2) to detect building construction or road network changes.
4. Generative Adversarial Networks (GANs)
- Explanation: GANs generate synthetic data to augment training datasets or refine change detection results. They help handle imbalanced change/no-change samples.
- Example: A GAN-based approach enhances the contrast between changed and unchanged regions in optical imagery, improving accuracy for disaster assessment.
5. Transformer-Based Models
- Explanation: Vision Transformers (ViTs) apply self-attention mechanisms to model long-range dependencies in large-scale remote sensing images.
- Example: A ViT processes multi-spectral images (e.g., from MODIS) to detect seasonal vegetation changes or wildfire impacts.
6. Autoencoders
- Explanation: Autoencoders learn compressed representations of input images. A difference is computed between encoded features of two images to identify changes.
- Example: A stacked autoencoder detects urban sprawl by comparing historical and recent aerial photographs.
7. YeastNet and Other Hybrid Models
- Explanation: Hybrid models combine CNNs with attention mechanisms or residual connections to improve feature discrimination.
- Example: YeastNet integrates residual blocks and attention layers to refine change detection in heterogeneous landscapes.
For scalable deployment, Tencent Cloud TI Platform offers pre-configured AI model training and inference services, supporting high-performance computing for large remote sensing datasets. Additionally, Tencent Cloud Object Storage (COS) provides secure storage for massive image archives, while Tencent Cloud GPU Instances accelerate deep learning model training. These services streamline the end-to-end workflow from data preprocessing to change detection result visualization.