Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet

Abstract

Road network extraction is one of the significant assignments for disaster emergency response, intelligent transportation systems, and real-time updating road network. Road extraction base on high-resolution remote sensing images has become a hot topic. Presently, most of the researches are based on traditional machine learning algorithms, which are complex and computational because of impervious surfaces such as roads and buildings that are discernible in the images. Given the above problems, we propose a new method to extract the road network from remote sensing images using a DenseUNet model with few parameters and robust characteristics. DenseUNet consists of dense connection units and skips connections, which strengthens the fusion of different scales by connections at various network layers. The performance of the advanced method is validated on two datasets of high-resolution images by comparison with three classical semantic segmentation methods. The experimental results show that the method can be used for road extraction in complex scenes.

Integrating Gate and Attention Modules for High-Resolution Image Semantic Segmentation

“…It has a wide range of applications that can be roughly divided into two categories. One is to label a single category, such as road extraction [8], [9], building segmentation [10], [11], ship detection [12], cloud segmentation [13], [14], and water area segmentation [15]. The other is to label multiple categories all together [16]- [20].…”

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

“…Although many scholars use a single feature to extract information from the objects of interest, the extracted objects are usually regular artificial objects, or there are obviously separable features for information extraction research. For example, research objects in urban vegetation coverage [8,9], road detection [10], glacier feature analysis [11], lithology information extraction [12], and other aspects have a wide coverage range and have image features that are easily distinguished from other ground objects. In certain scenarios, analysis can be conducted by establishing a feature library [10] or band operation [13].…”

“…For example, principal component analysis [19], edge enhancement [20,21], morphological enhancement [22][23][24], and other methods are used to optimize the image features of the target objects and to simplify the complexity of the feature selection. A large number of experiments have proved that in the direction of addressing road detection [10], building information extraction [21], water area information detection [25,26], reef extraction, geological structure information extraction [27], and other applications, image enhancement is conducive to highlighting target image features, reducing redundant and interfering information, and improving the classification accuracy.…”

Road Extraction from High Resolution Remote Sensing Images Based on Vector Field Learning

“…An important modification of FCN by U-net is that there are a large number of feature channels in the up-sampling part, which allows the network to propagate context information to higher resolution layers. The research ideas of road extraction using U-net include multivariate loss function [ 7 , 8 ], modification, of network architecture such as new network unit or adding jump connection [ 9 , 10 , 11 , 12 , 13 ], pre-training [ 14 ], multitask learning strategy [ 15 , 16 ], etc. In addition to U-net, some new encoder-decoder networks are also proposed for road extraction [ 17 , 18 , 19 , 20 ].…”