Hyperspectral Image Classification Based on Novel Hybridization of Spatial-Spectral-Superpixelwise Principal Component Analysis and Dense 2D-3D Convolutional Neural Network Fusion Architecture
Bibliographic record
Abstract
We propose a hybridized technique named Spatial-Spectral-Superpixelwise PCA-based Dense 2D-3D CNN Fusion Architecture (3SPCA-D-2D-3D-CNN), that deals with the detailed and complex study of dimensionality reduction and classification of Hyperspectal images (HSI). Our work is 2-fold: At first (1), 3SPCA is applied on raw HSI that adopts superpixels-based local reconstruction to filter the images, whereas PCA-based supplementary global features acquire the relevant and low-dimensional local features. Every HSI pixel is reconstituted by the pixels of its closest neighbors in the same superpixel block to reduce noise and improve spatial information. Next, PCA is conducted on every zone and the full HSI to get local and global features. The local-global and spatial-spectral properties are then concatenated. Secondly (2), the D-2D-3D-CNN fusion architecture is made up of three 3D convolution blocks, two 2D convolution blocks with varied kernel sizes and filters, and four fully connected (FC) dense layers, totaling nine distinguishing and information-enriched features. These features can generate precise class labels and apply them to the appropriate landcovers. The proposed method has been applied to three publicly available HSI landcover datasets, the Indian Pines, the Salinas Valley, and the Pavia University. It achieved respectively 98.33%, 99.99%, and 98.73% average accuracy scores. Due to its improved Feature Extraction capacity from a limited number of training samples and its classification performance with fewer epochs, this method outperforms other relevant state-of-the-art CNN-based methods.
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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 | 0.000 | 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 | 0.000 |
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".