Superpixel Segmentation-Based Evolutionary Multitasking Algorithm for Feature Selection of Hyperspectral Images
Bibliographic record
Abstract
Feature selection (FS) is a very important technique for hyperspectral image (HSI) classification, as successfully selecting informative features can significantly increase the learning performance while reducing the computational cost. However, most of the existing FS methods tend to treat the HSI as a whole for FS, which does not fully consider the unique characteristics of HSIs and disregards the fact that different feature classes possess varying preferences for features. Thus, this paper proposes a superpixel segmentation based evolutionary multitasking algorithm for FS of HSIs, called SS-EMT. First, the superpixel segmentation method is used to partition the original HSI into several superpixel blocks, which can preserve well the information of different classes of the original image. Second, in order to explore each superpixel block efficiently, an evolutionary multitasking algorithm using particle swarm optimization is designed, which treats each superpixel block as a subtask and then optimizes these subtasks collaboratively by transferring useful knowledge among related subtasks. In addition, a new individual evaluation mechanism is devised to obtain multiple high-quality feature subsets with different numbers of features simultaneously in a single run, thus reducing the computational cost. Finally, extensive experimental results on four common HSI datasets under three classifiers validate that our proposed method outperforms several state-of-the-art FS methods.
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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".