Object Recognition in Remote Sensing Images Based on Modified Backpropagation Neural Network
Bibliographic record
Abstract
In the area of remote sensing, one of the problems is how high-quality remote sensing images are automatically categorized and classified. There have been many suggestions for alternatives. Amongst these, there are drawbacks of approaches focused on low visual and intermediate visual characteristics. This article, therefore, adopts the deep learning method for classifying high-resolution remote sensing picture scenes to learn semantic knowledge. Most of the existing neural network convolution approaches are focused on the model of transfer training and there are comparatively like hidden Marco models, linear fitting methods, the creation of new neural networks based on the latest high-resolution remote sensing picture data sets. But in this paper, we used a modified backpropagation neural network is proposed to detect the objects in images. To test the performance of the proposed model we use two remote sensing data sets benchmark tests were done. The test-precision, precision, reminder, and F1 scores are all fine with the Assist data collection. The precision, precision, reminder, and F1 score are all enhanced on the SIRI-WHU dataset. The proposed system has better precision and robustness compared to the current approaches including the most conventional methods and certain profound learning methods to scene distinguish high-resolution remote sensing pictures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".