Abstract
Active queries is an active learning method used for classification of remote sensing images. It consists of three steps: hierarchical clustering, dendrogram division, and active label selection. The goal of active learning is to reduce the needed amount of labeled data while preserving classification accuracy. We propose to apply local segmentation as a new step preceding the hierarchical clustering. We are using the SLIC (simple linear iterative clustering) algorithm for dedicated image segmentation. This incorporates spatial knowledge which leads to an increased learning rate and reduces classification error. The proposed method is applied to six different areas of the Vaihingen dataset.
| Original language | English |
|---|---|
| Pages (from-to) | 165-173 |
| Number of pages | 9 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 4 |
| Issue number | 1W1 |
| DOIs | |
| State | Published - 30 May 2017 |
| Event | ISPRS Hannover Workshop 2017 on High-Resolution Earth Imaging for Geospatial Information, HRIGI 2017, City Models, Roads and Traffic , CMRT 2017, Image Sequence Analysis, ISA 2017, European Calibration and Orientation Workshop, EuroCOW 2017 - Hannover, Germany Duration: 6 Jun 2017 → 9 Jun 2017 |
Keywords
- Active Learning
- Active Queries
- Hierarchical Clustering
- Land Cover Classification
- Remote Sensing
- Segmentation