Search

Road extraction from the remote sensing image over mountainous areas is a difficult vision problem. In this letter, we propose a multiple feature fully convolutional network (MFFCN) on the basis of FCN for mountainous road extraction. The benefits of this model are twofold: first, MFFCN is a semantic segmentation model, which has deep convolutional networks. It avoids the problem of repeated storage and computational convolutions caused by the use of pixel blocks. Second, the MFFCN model could extract the spectral and terrain features. This method ensures the integrity and continuity of the road extraction results. The dataset is composed of GF-2 data and ASTER GDEM data in the Shigatse region of Tibet. We test our network on the dataset and compare it with four road extraction methods. The result shows that the proposed MFFCN is superior to all the comparing methods. [ABSTRACT FROM AUTHOR]

Snow in mountainous areas is a major source of surface water and groundwater recharge in the world. The water balance in mountainous regions is controlled by the interactions between the climate, cryospheric, and hydrological systems. Surface snow grain is a sensitive thermodynamic indicator of snowpack and plays an important role in the snow albedo. In mountainous regions, the complicated terrain conditions may introduce errors in the snow grain size estimated from satellite imagery. In this letter, an effective method based on the Snow Grain-Size and Pollution (SGSP) amount algorithm is proposed to estimate the surface snow grain size with careful topographic correction, using spectral reflectance data in Channel 5 (1.24 \mu \textm ) of the Moderate Resolution Imaging Spectrometer. The SGSP-estimated snow grain size was validated with in situ measurements collected from field campaigns in mountainous areas of Manasi River Basin, China, during the periods of snow accumulation and ablation from 2011 to 2015. The R^2 value of 0.90 and root mean squared error of 80.42~\mu \textm were obtained. [ABSTRACT FROM PUBLISHER]

The normalized difference snow index (NDSI), a key part of the snow-mapping algorithm for extracting snow information from remotely sensed imageries, has been frequently employed in acquiring snow cover extent in the past decades. A linear regression analysis has been frequently used in estimating snow cover fraction (SCF) on a subpixel basis by developing a statistical relationship between NDSI and SCF. In this letter, a comparison of NDSI, ratio snow index (RSI), and difference snow index (DSI) in estimating SCF is carried out in mountainous area of northwestern China. Three kinds of fitting methods, i.e., linear fitting, logarithmic curve fitting, and exponential curve fitting, are employed in this comparison for the purpose of finding out a best statistical fitting relationship between SCF and snow index. The results show that RSI and DSI are both good substitutes for NDSI in SCF estimation due to high R-squares, up to 0.83, of fitted lines between snow indexes and SCF, respectively. Furthermore, exponential fitting is considered to be of the highest robustness in the three fitting methods of interest for all the snow indexes studied. [ABSTRACT FROM PUBLISHER]