Identifying Building Rooftops in Hyperspectral Imagery Using CNN With Pure Pixel Index
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Bibliographic record
Abstract
Deep learning and traditional machine learning algorithms have been widely applied to enhance the classification accuracy in remote sensing images. However, due to the variety and changeability of buildings, identifying building rooftops based on remote sensing images is still a challenge. Taking advantage of hyperspectral remote sensing imagery and spectroscopy, we propose a deep Convolutional Neural Networks (CNN) approach with Pure Pixel Index (PPI) constraints, named CNNP, to identify building rooftops materials. The framework, which accepts two kinds of data cubes as input data, extract spectral and spatial information by using 1D CNN and 3D CNNs with different kernel size, respectively. After the feature extraction, aiming to identify different building materials, the output of the top layer is the input to a classifier in a ratio decided upon by the PPI of the central pixel. To verify the effectiveness, we use Hyperion and Push-broom Hyperspectral Imager (PHI) data sets that represent high and low spatial resolution images to compare our proposed method with other traditional remote sensing image classification approaches, such as: Support Vector Machine (SVM); Stacked Auto-Encoders (SAE); Deep Belief Network (DBN); 1D CNN; and 2D CNN; 3D CNN; MiniGCN. The quantitative and qualitative results show that compared to other representative methods, CNNP achieves better performance, for both kinds of data, on Hyperion and PHI data sets with Overall Accuracy (OA) of 98.83% and 99.82%, respectively. And, the proposed method also provides an innovative idea for constructing other frameworks of hyperspectral image classification
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it