A Cross Entropy Based Deep Neural Network Model for Road Extraction from Satellite Images

Abstract

This paper proposes a deep convolutional neural network model with encoder-decoder architecture to extract road network from satellite images. We employ ResNet-18 and Atrous Spatial Pyramid Pooling technique to trade off between the extraction precision and running time. A modified cross entropy loss function is proposed to train our deep model. A PointRend algorithm is used to recover a smooth, clear and sharp road boundary. The augmentated DeepGlobe dataset is used to train our deep model and the asynchronous training method is applied to accelerate the training process. Five salellite images covering Xiaomu village are taken as input to evaluate our model. The proposed E-Road model has fewer number of parameters and shorter training time. The experiments show E-Road outperforms other state-of-the-art deep models with 5.84% to 59.09% improvement, and can give the accurate predictions for the images with complex environment.

Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations

“…We concluded that these imperfections were caused by the complex nature of the geospatial object (roads have large curvature changes, different materials used in the pavement, different widths, depending on the importance of the route, and very often have no clearly defined borders) by the presence of occlusions in the scenes, and by the limitation of existing semantic segmentation algorithms. These imperfections and errors are in line with issues raised by other investigations, as similar problems were identified in other works tackling the road extraction task from high-resolution remote sensing images [2], [3], [4], [5], and are very problematic when pursuing a large-scale road extraction operation for automatic mapping purposes. As a consequence, we consider that adding a post-processing operation to improve the initial segmentation predictions is essential for a successful road extraction.…”

Generative Learning for Postprocessing Semantic Segmentation Predictions: A Lightweight Conditional Generative Adversarial Network Based on Pix2pix to Improve the Extraction of Road Surface Areas

“…We also identified higher rates of "false positive labels in areas where the materials used in the road pavement have a similar spectral signature with their surroundings, or areas where geospatial objects with similar features are present (such as dry riverbeds, railroads, or irrigation canals) and higher rates of false negatives in sections where other objects cover large portions of the roads were covered" (page 13 in [8]). Similar problems are still observed in recent works dealing with the road extraction from high-resolution aerial imagery-improving the road extraction task is an active area of research [11][12][13][14].…”

Entropy-Based Algorithms for Signal Processing

“…The contribution by Shan and Fang [ 2 ], “A Cross Entropy Based Deep Neural Network Model for Road Extraction from Satellite Images,” proposes a deep convolutional neural network model with an encoder-decoder architecture to extract a road network from satellite images. The authors employ ResNet-18 and atrous spatial pyramid pooling technique to tradeoff between the extraction precision and running time.…”