Using locality-constrained linear coding in automatic target detection of HRS images
Bibliographic record
Abstract
Automatic target detection with complicated shapes in high spatial resolution images is an ongoing challenge in remote sensing image processing. This is because most methods use spectral or texture information, which are not sufficient for detecting complex shapes. In this paper, a new detection framework, based on Spatial Pyramid Matching (SPM) and Locality- constraint Linear Coding (LLC), is proposed to solve this problem, and exemplified using airplane shapes. The process starts with partitioning the image into sub-regions and generating a unique histogram for local features of each sub-region. Then, linear Support Vector Machines (SVMs) are used to detect objects based on a pyramid-matching kernel, which analyses the descriptors inside patches in different resolution. In order to generate the histogram, first a point feature detector (e.g. SIFT) is applied on the patches, and then a quantization process is used to select local features. In this step, the k-mean method is used in conjunction with the locality-constrained linear coding method. The LLC forces the coefficient matrix in the quantization process to be local and sparse as well. As a result, the speed of the method improves around 24 times in comparison to using sparse coding for quantization. Quantitative analysis also shows improvement in comparison to just using k-mean, but the accuracy in comparison to using sparse coding is similar. Rotation and shift of the desired object has no effect on the obtained results. The speed and accuracy of this algorithm for high spatial resolution images make it capable for use in real-world applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".