# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on Hyperspectral Images
#### 3.2.1 Main Methods  
Most road extraction methods using multispectral images are based on high-spatial-resolution images, and these methods can also be divided into heuristic and data-driven methods. Similar to the road extraction from high-resolution images, the heuristic methods in this case can also be divided into semiautomatic and automatic categories; some applications of these heuristic methods are reported in [184; 185; 186; 187; 188; 189]. However, few data-driven methods are exclusively used for road extraction based on multispectral images, owing to the lack of public datasets. There are fewer road extraction methods based on hyperspectral images than on high-resolution images, and some of them are included in hyperspectral classifications. In this paper, we introduce road extraction methods using hyperspectral images based on different platforms. The spaceborne hyperspectral imagers can realise a larger swath and provide a better stability platform than do the airborne instruments; hence, the spaceborne hyperspectral imagers are suitable for large-area work. However, they are mainly used to identify and extract arterial roads such as highways because of their low spatial resolution. The Hyper-ion hyperspectral imager of the US--EO-1 satellite--is the currently available spaceborne hyperspectral sensor for road extraction. [PERSON] [190] used Hyperion hyperspectral data to complete the road network extraction from an image using three steps: road searching, road tracking and road connecting. In road searching, the spectral information in the image is used to find road features, and several different road feature extraction methods are qualitatively compared; however, no quantitative extraction accuracy results are provided in [PERSON]'s report. Airborne hyperspectral imagers can achieve higher spatial and spectral resolutions than do the spaceborne platforms; however, their operating efficiency is lower than that of the spaceborne instruments. They are mainly used for the extraction of urban roads and detection of road conditions. Airborne hyperspectral instruments currently used for road extraction mainly include AVIRIS, CASI, HyMap, HYDICE and AsiaFenix [122]. In 2001, [PERSON] et al. [191] used the hyperspectral dataset of AVIRIS in Santa Barbara, USA, to map the different types of typical urban surface features through multiterminal spectral analysis. Then, the Q-tree filter was used to distinguish between the roofs and the roads constructed using the same materials and exhibiting similar spectra. The visible results showed that the main roads in the image can be preliminarily extracted; however, several shortcomings were observed in the extraction of roads blocked by vegetation and connectivity of the road network. [PERSON] et al. [192] used the urban hyperspectral dataset of AVIRIS and a spectral database, based on the surface materials of the main urban features collected in the field, to extract the road centreline and observe the road surface conditions. Furthermore, the optimal parameters for designing a multispectral instrument to extract urban land-use types was proposed based on these reported results. The overall classification accuracy and kappa coefficient were 73.5% and 72.5%, respectively [192]. Based on the airborne hyperspectral image data of HYDICE and HyMap, [PERSON] and [PERSON] [193] used an adaptive mean-shift method to accurately classify six major urban features in the image, including roads, houses, and grass. The overall classification accuracy was above 97%, and the road classification accuracy was above 95%. [PERSON] et al. [194] used CASI-1500 airborne hyperspectral data to study the extraction of asphalt roads in cities. The results based on ISODATA unsupervised classification and maximum likelihood supervised classification methods qualitatively showed that the hyperspectral image could be used to extract the main asphalt roads in the city; however, they did not report the extraction accuracy.