水体分割 transformer英语翻译

Waterbody segmentation transformer refers to a deep learning-based method, typically in the field of computer vision and remote sensing, that employs a transformer architecture to perform the task of segmenting or separating waterbodies from satellite or aerial imagery. Transformers, originally introduced in natural language processing by Google's BERT model, have been adapted for image analysis due to their ability to capture long-range dependencies effectively.

In this context, the transformer model processes the image data, often with a sequence of patches or encoded features, and learns to classify each pixel as belonging to a waterbody (e.g., ocean, lakes, rivers) or not. The network is trained using labeled datasets where the boundaries of water bodies are annotated, allowing it to discern and isolate water areas within the image.