Toward Multitask Perception for Remote Sensing Imagery via Compression and Prompt Tuning
Bibliographic record
Abstract
Recently, advancements in satellite technology have greatly increased the availability of high-resolution remote sensing images. Concurrently, learning-based image compression (LIC) has significantly improved the efficiency of transmitting and storing such images. As machine recognition tasks increasingly depend on transmitting visual data across devices, compressed images play a key role in both human and machine perception during downstream tasks. However, most LIC approaches are not optimized for machine recognition tasks. To address this limitation, we propose a remote sensing image compression network called RSIC, which integrates multi-task perception and supports downstream tasks such as object detection. Specifically, we introduce a wavelet-based frequency-spatial block (WFSB) that separates frequency components and processes them using Transformer and CNN blocks to effectively capture frequency-specific features. Within WFSB, the Prompting Swin-Transformer Block (PSTB) extracts spatial information while enabling prompt tuning. Additionally, after primary codec training, instance and task prompts are applied during the encoding and decoding stages, respectively, facilitating machine perception without full fine-tuning. Extensive experimental results show that our model achieves better rate-distortion performance for image compression on the AID test dataset, surpassing the traditional VVC codec and several recent LIC methods. Furthermore, our method demonstrates superior performance in terms of rate-accuracy for machine perception on the NWPU VHR-10 and HRSID remote sensing datasets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".