Convolutional Neural Network-Based Deep Learning Approach for Automatic Flood Mapping Using NovaSAR-1 and Sentinel-1 Data
Bibliographic Data
| ID | 22032465 |
|---|---|
| Authors | Ogbaje Andrew (0009-0007-3166-7747, University of Southern Queensland, corresponding author), Armando Apan (0000-0002-5412-8881, University of Southern Queensland), Dev Raj Paudyal (0000-0003-1010-2118, University of Southern Queensland), Kithsiri Perera (University of Southern Queensland) |
| Year | 2023 |
| Volume | 12 |
| Issue | 5 |
| Pages | 194 |
| Publication date | 2023-05-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi12050194 |
| OpenAlex | W4382490431 |
| Language | EN |
| Citations received | 3 |
| References cited | 53 |
The accuracy of most SAR-based flood classification and segmentation derived from semi-automated algorithms is often limited due to complicated radar backscatter. However, deep learning techniques, now widely applied in image classifications, have demonstrated excellent potential for mapping complex scenes and improving flood mapping accuracy. Therefore, this study aims to compare the image classification accuracy of three convolutional neural network (CNN)-based encoder–decoders (i.e., U-Net, PSPNet and DeepLapV3) by leveraging the end-to-end ArcGIS Pro workflow. A specific objective of this method consists of labelling and training each CNN model separately on publicly available dual-polarised pre-flood data (i.e., Sentinel-1 and NovaSAR-1) based on the ResNet convolutional backbone via a transfer learning approach. The neural network results were evaluated using multiple model training trials, validation loss, training loss and confusion matrix from test datasets. During testing on the post-flood data, the results revealed that U-Net marginally outperformed the other models. In this study, the overall accuracy and F1-score reached 99% and 98% on the test data, respectively. Interestingly, the segmentation results showed less use of manual cleaning, thus encouraging the use of open-source image data for the rapid, accurate and continuous monitoring of floods using the CNN-based approach
Confusion matrix · Convolutional neural network · Data mining · Database · Deep learning · Flood myth · Machine learning · Segmentation · Test data · Transfer of learning · Workflow · Computer Science · Flood Risk Assessment and Management · Groundwater and Watershed Analysis · Tropical and Extratropical Cyclones Research · Artificial Intelligence
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |