Multiscale Feature Fusion for Hyperspectral Image Classification Using Hybrid 3D-2D Depthwise Separable Convolution Networks
Bibliographic record
Abstract
Hyperspectral remote sensing images (HRSI) comprise three-dimensional image cubes, containing a single spectral dimension alongside two spatial dimensions.HRSI are presently among the foremost essential datasets for Earth observation.The task of HRSI classification is intricate due to the influence of spectral mixing, leading to notable variability within classes and resemblances across classes.Consequently, the field of HRSI classification has garnered significant research attention in recent times.Convolutional Neural Networks (CNNs) are harnessed to address these issues, enabling both feature extraction and classification.This study introduces a novel approach for HRSI classification called the hybrid 3D-2D depthwise separable convolution network (Hybrid DSCNet), which leverages multiscale feature integration.Within the Hybrid DSCNet, diverse kernel sizes contribute to an enriched feature extraction process from HRSI.The conventional 3D-2D CNN, while effective, comes with a computational load.Instead of using the standard 3D-2D CNN, this study adopts the 3D-2D DSC architecture.This approach partitions the conventional convolution into two components: pointwise and depthwise convolution, yielding a substantial reduction in trainable parameters and computational complexity.To evaluate the proposed method, the Indian Pines dataset along with WHU-Hi subdatasets (LongKou-LK, HanChuan-HC, and HongHu-HH) were employed.Employing a 5% training sample, impressive overall accuracy scores were achieved: 94.51%, 99.78%, 97.06%, and 97.27% for Indian Pines, WHU-LK, WHU-HC, and WHU-HH, respectively.Comparative analysis of the proposed approach with cutting-edge techniques within the literature reveals its superior performance across the four HRSI datasets.Notably, the Hybrid DSCNet attains enhanced classification accuracy while maintaining lower computational overhead.
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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 | 0.001 |
| 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".