Resource Efficient Framework for Remote Sensing Visual Recognition
Bibliographic record
Abstract
In the rapidly evolving field of remote sensing (RS), the need for efficient and accurate scene classification is paramount. RS imagery comprising satellite and aerial imagery often faces challenges such as varying scales and diverse environmental conditions, which can significantly affect the discernibility of important features. To address these challenges, this article introduces a lightweight dual-branch network architecture that adequately handles scale variations and complex scene compositions. The first branch, progressive feature processing branch (PFPB), of the proposed framework is engineered to extract rich multiple-scale features through collaborative parallel stages and intrabranch and interbranch connectivity with optimized computational resources. The second branch, InXformer branch (IXB) enhances the system’s capability to assimilate global context and long-range dependencies essential for comprehensive scene analysis utilizing an involution-based transformer approach. Experimental validation in three challenging datasets sourced from diverse aerial platforms demonstrates the greater effectiveness of the proposed network. The proposed network achieves a weighted${F}1$of 97.15% in the AIDERSv2 dataset, surpassing other methods such as DecoupleNet by more than 2%, while maintaining high efficiency with 0.41M parameters, lower computational overhead with 0.96 GFLOPs, and a higher processing speed of 4616 frames/s (FPS). With regards to WHU-RS19 and UCM datasets, the devised network achieves 93.69% and 94.57% weighted-${F}1$score, respectively. These results underscore the ability of the proposed network to efficiently handle diverse scene compositions by delivering state-of-the-art performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".