Research Image Processing
Change detection via semantic segmentation and difference extraction method
We aim to develop a fast and accurate method of updating maps by detecting local differences occurred over different time series; where only region with differences will be updated. In our system, aerial imageries from Massachusetts's building open datasets are used as training datasets; meanwhile Saitama district datasets are used as input images. Semantic segmentation is then applied to input images to get predicted map patches of building. Semantic segmentation is a pixel-wise classification of images by implementing convolutional neural network technique. Convolutional neural network technique is implemented due to being not only efficient in learning highly discriminative image features such as buildings, but also partially robust to incomplete and poorly registered target maps. Next, in order to understand overall changes occurred in an area, both semantic segmented images from the same scene are undergone change detection method. Lastly, difference extraction method is implemented to specify the category of building changes.