Cross-Satellite Semantic Segmentation With Bidirectional Adaptation
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Solution Overview
Problem
Existing convolutional neural networks for semantic segmentation of remote sensing images struggle when applied across different satellite domains due to domain shift, leading to unsatisfactory performance, and existing methods for domain adaptation, such as data augmentation and generative adversarial networks, are limited in effectiveness and stability.
Innovation Solution
A semantic segmentation method using unsupervised bidirectional domain adaptation and fusion, involving training bidirectional image generators with cycle consistency, adversarial, and semantic consistency loss functions, followed by selecting high-quality generators and fusing segmentation probabilities to improve cross-satellite image segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a model is trained on source domain images and directly applied to target domain images, then training cost is reduced, but segmentation accuracy drops sharply due to domain shift
Solution Approach 1:
The patent introduces bidirectional image generators as intermediary components that translate images between source and target domains. The generator G_s2t translates source images to target domain style, while G_t2s translates target images to source domain style. These intermediaries enable the model to adapt to domain shifts without retraining on expensive target domain labeled data, thus maintaining segmentation accuracy while avoiding direct target domain training costs.
Solution Approach 2:
The patent changes the domain parameters of images through bidirectional translation. By applying domain adaptation transformations that modify spectral characteristics, atmospheric conditions, and sensor response parameters, the model can process images from different satellites. This parameter transformation approach allows the source domain model to handle target domain images with different data distributions without retraining.
2Adaptability or versatility
If generative adversarial network is used to generate pseudo-target domain images, then domain adaptation is improved, but model stability deteriorates due to mode collapse
Solution Approach 1:
The patent applies counterbalancing strategies to offset the instability of GANs. It uses multiple loss functions (adversarial loss, cycle consistency loss, semantic consistency loss, and identity loss) that work together to stabilize training. The cycle consistency constraint ensures that translating an image from source to target domain and back yields the original image, preventing mode collapse. Additionally, the patent uses pre-trained generators and selective epoch usage to further stabilize the adaptation process.
Solution Approach 2:
The patent implements feedback mechanisms through cycle consistency loss and semantic consistency loss. The cycle consistency loss provides feedback by comparing the original source image with the translated-back image, ensuring the generator maintains important visual information. The semantic consistency loss provides feedback by comparing segmentation results before and after translation, ensuring semantic information is preserved. These feedback loops stabilize the GAN training and prevent mode collapse.
3Adaptability or versatility
If image translation is performed to adapt domains, then domain shift is reduced, but semantic information may be changed during translation
Solution Approach 1:
The patent uses the target domain image as an intermediary reference during translation. Instead of directly translating source images to target style without guidance, the method conditions the translation process on the actual target domain image, ensuring the generated pseudo-target images maintain semantic consistency with the original target images while adapting the domain style.
Solution Approach 2:
The patent employs semantic consistency loss as a feedback mechanism that compares segmentation results obtained from original source images with those from translated images. This feedback ensures that the translation process preserves semantic information by penalizing changes in segmentation outcomes. The loss function guides the generator to maintain semantic fidelity while achieving domain adaptation.
4Measurement precision
If segmentation results from source and target domains are fused, then segmentation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies partial fusion strategies rather than completely fusing all segmentation results. It uses a fusion coefficient that controls the weighting between source domain and target domain segmentation results. This partial fusion approach achieves improved accuracy by incorporating beneficial information from both domains while avoiding the full computational overhead of complete fusion, allowing flexible trade-off between accuracy and complexity.
Data Source
AI summary
The present invention discloses a semantic segmentation method for cross-satellite remote sensing images based on unsupervised bidirectional domain adaptation and fusion. The method includes training of bidirectional source-target domain image translation models, selection of bidirectional generators in the image translation models, bidirectional translation of source-target domain images, training of source and target domain semantic segmentation models, and generation and fusion of source and target domain segmentation probabilities. According to the present invention, by utilizing source-target and target-source bidirectional domain adaptation, the source and target domain segmentation probabilities are fused, which improves the accuracy and robustness of a semantic segmentation model for the cross-satellite remote sensing images; and further, through the bidirectional semantic consistency loss and the selection of the parameters of the generators, the influence due to the instability problem of the generators in the bidirectional image translation models is avoided.