Image Translation Network for Remote Sensing Semantic Segmentation
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Solution Overview
Problem
Existing unsupervised image translation methods for high-resolution remote sensing images fail to consider the specific task requirements, leading to over-interpretation or over-simplification of cross-domain data, which affects the accuracy of semantic segmentation tasks.
Innovation Solution
A method and system that utilize an image translation network model with a combined objective loss function, including image translation loss and model adaptive loss, to fine-tune the translation process, ensuring that the translated images align with the target domain's distribution and adapt to the specific task model, thereby preventing over-interpretation or over-simplification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unsupervised image translation method is used to transfer cross-domain data, then the domain difference between source and target images is reduced, but the translated images may be over-interpreted or over-simplified, losing task-specific features
Solution Approach 1:
The patent introduces a feedback mechanism where the translated images are fed back into the semantic segmentation model, and the model's performance on these translated images provides feedback to guide the translation process. This is achieved through iterative optimization where the translation parameters are adjusted based on segmentation performance metrics, ensuring that translated images retain task-relevant features while adapting to the target domain
Solution Approach 2:
The patent creates a multi-functional system where the image translation model serves multiple purposes: it adapts domain differences between source and target images while simultaneously preserving task-specific features needed for semantic segmentation. The system achieves this by integrating the translation and segmentation processes, allowing the same framework to handle both domain adaptation and feature preservation
2Measurement precision
If existing deep learning models are trained on specific domain data, then detection performance on that domain is improved, but generalization ability to cross-domain data deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the translation parameters and model weights during the iterative optimization process. The system modifies parameters such as translation strength, loss function weights, and learning rates to balance between adapting to the target domain and preserving source domain features, enabling the model to generalize across domains while maintaining detection performance
3Measurement precision
If manual annotation is performed on cross-domain data to improve model training, then detection accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate and refine its own training data through unsupervised translation. The model translates source domain images to target domain style without manual annotation, then uses these translated images for training, creating a self-sufficient pipeline that eliminates the need for time-consuming manual labeling while maintaining detection accuracy
Data Source
AI summary
The present invention discloses a method and system for directed transfer of cross-domain data based on high-resolution remote sensing images. In the method of the present invention, first, an objective loss function which combines an image translation loss and a model adaptive loss of an image translation network model is established, thus overcoming the technical shortcoming that an existing data translation technique fails to take a specific task into full consideration and ignores a negative impact of data translation on the specific task. Further, a trained image translation network model is fine-tuned based on sample data, so that the image translation network model keeps translation towards the effect desired by the target model, thus avoiding over-interpretation or over-simplification during directed transfer of cross-domain data and improving accuracy of directed transfer of the cross-domain data based on the high-resolution remote sensing images.