Remote Sensing

In this paper, we introduce an improved deep convolutional encoder-decoder network (DCED) for segmenting road objects from aerial images. Enhancements include the use of ELU (exponential linear unit) instead of ReLU, dataset augmentation with incrementally-rotated images to increase training data by eight times, and the use of landscape metrics to remove false road objects. Tested on the Massachusetts Roads dataset, our method outperformed the SegNet benchmark and other baselines in precision, recall, and F1 scores.

Data extraction of geo-spatial objects from satellite images is a crucial step in facilitating analysis of geo-spatial or spatio-temporal data, typically involving line (road) and polygon (area) layers. This paper introduces a method for transforming satellite data (raster images) containing roads from pixel form into spatial objects comprising lines and polygons. Our algorithm consists of three primary steps. First, roads are isolated from other objects using k-means clustering. Second, line extraction is performed on the road areas by applying morphological operations to skeletonize the image, followed by enhancement using the Ramer-Douglas-Peucker algorithm. Finally, land-cover classification is applied to non-road objects to extract polygons. Experimental results demonstrate that both lines (road networks) and polygons (areas) can be accurately extracted from satellite imagery simultaneously.