Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

We propose an improved visibility graph method, i.e., limited penetrable visibility graph, for establishing complex network from time series. Through evaluating the degree distributions of three visibility algorithms(visibility graph, horizontal visibility graph, limited penetrable visibility graph), we find that the horizontal visibility graph cannot distinguish signals from periodic, fractal, and chaotic systems; for fractal signal, the degree distributions obtained from visibility graph and limited penetrable visibility both can be well fitted to a power-law(scale-free distribution), but the anti-noise ability is not good; for periodic and chaotic signals, the limited penetrable visibility graph shows better anti-noise ability than visibility graph. In this regard, we use the limited penetrable visibility graph to extract the network degree distribution parameters from conductance fluctuating signals measured from oil-gas-water three-phase flow test. The results indicate that combination parameters of network degree distribution can be used to classify typical three phase flow patterns, e.g., oil-in-water bubble flow, bubble-slug transitional flow and slug flow.

Traditional road extraction algorithms, which focus on improving the accuracy of road surfaces, cannot overcome the interference of shelter caused by vegetation, buildings, and shadows. In this paper, we extract the roads via road centerline extraction, road width extraction, broken centerline connection, and road reconstruction. We use a multiscale segmentation algorithm to segment the images, and feature extraction to get the initial road. The fast marching method (FMM) algorithm is employed to obtain the boundary distance field and the source distance field, and the branch backing-tracking method is used to acquire the initial centerline. Road width of each initial centerline is calculated by combining the boundary distance fields, before a tensor field is applied for connecting the broken centerline to gain the final centerline. The final centerline is matched with its road width when the final road is reconstructed. Three experimental results show that the proposed method improves the accuracy of the centerline and solves the problem of broken centerline, and that the method reconstructing the roads is excellent for maintain their integrity.

An in situ soil moisture observation network at pixel scale is constructed in cropland in the northeast of China for accurate regional soil moisture evaluations of satellite products. The soil moisture products are based on the Japan Aerospace Exploration Agency (JAXA) algorithm and the Land Parameter Retrieval Model (LPRM) from the Advanced Microwave Scanning Radiometer 2 (AMSR2), and the products from the FengYun-3B (FY3B) satellite are evaluated using synchronous in situ data collected by the EC-5 sensors at the surface in a typical cropland in the northeast of China during the crop-growing season from May to September 2017. The results show that the JAXA product provides an underestimation with a bias (b) of -0.094 cm3/cm3, and the LPRM soil moisture product generates an overestimation with a b of 0.156 cm3/cm3. However the LPRM product shows a better correlation with the in situ data, especially in the early experimental period when the correlation coefficient is 0.654, which means only the JAXA product in the early stage, with an unbiased root mean square error (ubRMSE) of 0.049 cm3/cm3 and a b of -0.043 cm3/cm3, reaches the goal accuracy (±0.05 cm3/cm3). The FY3B has consistently obtained microwave brightness temperature data, but its soil moisture product data in the study area is seriously missing during most of the experimental period. However, it recovers in the later period and is closer to the in situ data than the JAXA and LPRM products. The three products show totally different trends with vegetation cover, soil temperature, and actual soil moisture itself in different time periods. The LPRM product is more sensitive and correlated with the in situ data, and is less susceptible to interferences. The JAXA is numerically closer to the in situ data, but the results are still affected by temperature. Both will decrease in accuracy as the actual soil moisture increases. The FY3B seems to perform better at the end of the whole period after data recovery.

For multi-spectral remote sensing imagery, accurate shadow extraction is of great significance for overcoming the information loss caused by high buildings and the solar incidence angle in urban remote sensing. However, diverse solar illumination conditions, similarities between shadows, and other dark land features bring uncertainties and deviations to shadow extraction processes and results. In this paper, we classify shadows as either strong or weak based on the ratio between ambient light intensity and direct light intensity, and use the fractal net evolution approach (FNEA), which is a multi-scale segmentation method based on spectral and shape heterogeneity, to reduce the interference of salt and pepper noise and relieve the error of misdiagnosing land covers with high reflectivity in shaded regions as unshaded ones. Subsequently, an object-based shadow index (OSI) is presented according to the illumination intensities of different reflectance features, as well as using the normalized difference water index (NDWI) and near infrared (NIR) band to highlight shadows and eliminate water body interference. The data from three high-spatial-resolution satellites—WorldView-2 (WV-2), WorldView-3 (WV-3), and GaoFen-2 (GF-2)—were used to test the methods and verify the robustness of the OSI. The results show that the OSI index performed well regarding both strong and weak shadows with the user accuracy and the producer accuracy both above 90%, while the four other existing indexes that were tested were not effective at diverse solar illumination conditions. In addition, all the disturbances from water body were excluded well when using the OSI, except for the GF-2 data in weak shadows.