Segmentation and Classification Using Logistic Regression in Remote Sensing Imagery

This paper presents techniques for segmentation and change classification using logistic regression. The research was conducted on SPOT 5 multispectral multitemporal images covering the 2010 floods in Pakistan. Segmentation was performed to extract the built up area (BUA) from the satellite images and change detection was performed to find the damaged BUA. The damaged area was classified into three categories based on the extent of damage. The segmentation results were validated using statistical measures like precision, recall, and dice coefficient on available ground truth. The results of change classification were compared and found consistent with the manual assessment report produced by UNO experts using Worldview 1 satellite imagery with submeter resolution. The proposed scheme and results give an indication that SPOT 5 imagery can be used for fast automatic damage assessment and classification immediately after a natural calamity. The proposed change detection technique was also applied on Unites States Geographical Survey dataset. We compared our change detection results with established methods like change vector analysis, Principal component analysis using K-means and commercially available software Erdas Imagine on both the above-mentioned datasets. The comparison results suggest that our proposed algorithm performs better than the other methods.