Semantic Segmentation of Land Use / Land Cover (LU/LC) Types Using F-CNNS on Multi-Sensor (Radar-Ir-Optical) Image Data
Bibliographic record
Abstract
Land Use/ Land Cover (LU/LC) segmentation is a widely studied topic in the field of remote sensing. Past focus has been on independent studies either on color (RGB) and the Normalized Vegetation Index (NDVI) or on Polarimetric Synthetic Aperture Radar (PolSAR) data. In this paper we explore the fusion potential of RGB images with additional SAR and Near Infra-red (NIR) images for enhanced LU/LC segmentation through Fully-Convolutional Neural Networks (F-CNNs). F-CNNs have been extensively studied for semantic segmentation problems with U-Net and SegNet being two well-known F-CNN architectures. Both these architectures were used as references for this study. High resolution RGB, SAR and NIR images were acquired through Google Earth (GE), German Aerospace Center (DLR) and The Planet Laboratories, respectively. IR was converted to NDVI for its higher potential of segmentation of vegetations areas. Four multi-sensor configurations as input channels to the networks were studied after precise co-registration of these images, and the results were compared to individual channels for both architectures. Simon Fraser University (SFU), Burnaby Campus and its surrounding area was selected for this study due its diverse land types. The area was divided into 5 classes i.e. Roads, Buildings, Forest, Water and No class (unclassified). An overall, best accuracy of ~86% was achieved for a five-channel configuration (R+G+B+SAR+NDVI). We show that the inclusion of SAR and IR channels to RGB based network can significantly improve the performance of LU/LC segmentation.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".