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Exploring Text-Guided Single Image Editing for Remote Sensing Images
Computer Vision and Pattern Recognition · Submitted 9 May 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Experimental results on multiple RSI editing tasks show that the proposed method offers significant advantages in both CLIP scores and subjective evaluations compared to existing methods.
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The abstract, sentence by sentence
Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. For natural images, these stages primarily rely on generative backbones pre-trained on large-scale benchmark datasets and text guidance facilitated by vision-language models (VLMs). However, it become less viable for RSIs: First, existing generative RSI benchmark datasets do not fully capture the diversity of RSIs, and is often inadequate for universal editing tasks. Second, the single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics. To solve above problems, this paper proposes a text-guided RSI editing method and can be trained using only a single image. A multi-scale training approach is adopted to preserve consistency without the need for training on extensive benchmarks, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability. Experimental results on multiple RSI editing tasks show that the proposed method offers significant advantages in both CLIP scores and subjective evaluations compared to existing methods. Additionally, we explore the ability of the edited RSIs to support disaster assessment tasks in order to validate their practicality. Codes will be released at https://github.com/HIT-PhilipHan/remote_sensing_image_editing.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. | gap 0.99 | 0.09 | 0.02 | asserted 1.00 |
| s2 | Deep learning based editing methods generally involve two sequential stages: generation and editing. | background 0.99 | 0.06 | 0.02 | asserted 1.00 |
| s3 | For natural images, these stages primarily rely on generative backbones pre-trained on large-scale benchmark datasets and text guidance facilitated by vision-language models (VLMs). | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s4 | However, it become less viable for RSIs: First, existing generative RSI benchmark datasets do not fully capture the diversity of RSIs, and is often inadequate for universal editing tasks. | gap 0.49 | 0.11 | 0.03 | asserted 0.98 |
| s5 | Second, the single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics. | background 0.59 | 0.09 | 0.02 | asserted 0.99 |
| s6 | To solve above problems, this paper proposes a text-guided RSI editing method and can be trained using only a single image. | contribution 0.98 | 0.89 | 0.25 | asserted 1.00 |
| s7 | A multi-scale training approach is adopted to preserve consistency without the need for training on extensive benchmarks, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability. | method 0.99 | 0.90 | 0.09 | asserted 1.00 |
| s8 | Experimental results on multiple RSI editing tasks show that the proposed method offers significant advantages in both CLIP scores and subjective evaluations compared to existing methods. | result 1.00 | 0.95 | 0.65 | asserted 1.00 |
| s9 | Additionally, we explore the ability of the edited RSIs to support disaster assessment tasks in order to validate their practicality. | contribution 0.82 | 0.91 | 0.06 | asserted 0.59 |
| s10 | Codes will be released at https://github.com/HIT-PhilipHan/remote_sensing_image_editing. | future work 0.74 | 0.41 | 0.02 | asserted 0.93 |
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The paper
Original abstract
Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. For natural images, these stages primarily rely on generative backbones pre-trained on large-scale benchmark datasets and text guidance facilitated by vision-language models (VLMs). However, it become less viable for RSIs: First, existing generative RSI benchmark datasets do not fully capture the diversity of RSIs, and is often inadequate for universal editing tasks. Second, the single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics. To solve above problems, this paper proposes a text-guided RSI editing method and can be trained using only a single image. A multi-scale training approach is adopted to preserve consistency without the need for training on extensive benchmarks, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability. Experimental results on multiple RSI editing tasks show that the proposed method offers significant advantages in both CLIP scores and subjective evaluations compared to existing methods. Additionally, we explore the ability of the edited RSIs to support disaster assessment tasks in order to validate their practicality. Codes will be released at https://github.com/HIT-PhilipHan/remote_sensing_image_editing.
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References
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What this paper does with the arXiv papers it cites
What it cites · 3 sentences without a confident label
Uses the method or tool of 2 papers
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For the selection of VLMs, in order to introduce the semantic information of RSIs, we use the RemoteCLIP model [ref] , which is fine-tuned on remote sensing big data.
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing · Experiment Settings · 1.00
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Howerver, it is difficult to directly make the model predict $μ_θ$, because the distribution of the $μ_θ$ is relatively uncertain and has a wide range of values, so the above prediction of the mean can be converted into a prediction of $ϵ_θ$.Then, through reparameterization techniques [ref] , the following simple loss function can be obtained: [equation] where the $ϵ_θ$ is prediction of the fully convolutional model.
Denoising Diffusion Probabilistic Models · Multi-scale training strategy based on single image · 0.90
Cites as background 2 papers
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For instance, the seminal work in VLMs, contrastive language-image pre-training (CLIP), achieves semantic alignment between text and images through pre-training on large-scale paired text-image datasets [ref] , which is essential for text-guided image editing.
Learning Transferable Visual Models From Natural Language Supervision · Introduction · 0.85
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Generally, such methods can be classified into below categories [ref] : training-based approaches [ref] , testing-time fine-tuning approaches [ref] , training and fine-tuning free approaches [ref] .
Diffusion Model-Based Image Editing: A Survey · Introduction · 0.96
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