Improved Narrow Water Extraction Using a Morphological Linear Enhancement Technique

An improved water extraction method using a morphological linear enhancement technique is proposed to improve the delineation of narrow water features for the modified normalized difference water index (MNDWI) derived from remote sensing images. This method introduces a morphological white top-hat (WTH) transforming operation on the MNDWI to extract multi-scale and multidirectional differential morphological profiles and constructs a morphological narrow water index (MNWI). The MNWI can effectively enhance the local contrast of linear objects, allowing narrow water bodies to be easily separated from mountain shadows and other features. Furthermore, to accurately delineate surface water bodies, a dual-threshold segmentation method was also developed by combining an empirical threshold segmentation with the MNDWI for wide water bodies and an automatic threshold segmentation with the MNWI for narrow water bodies. This method was validated using three experimental datasets, which were taken from two different Landsat images. Our results demonstrate that narrow water bodies can be sufficiently identified, with an overall accuracy of over 90%. Most narrow streams or rivers keep a continuous shape in space, and the boundaries of the water bodies are accurately delineated as compared with the MNDWI method. Finally, the proposed method was used to extract the entire inland surface water of Fujian province, China

Fusion of Rotation-Invariant Texture Features for Scene Recognition

classification. However, the two above methods make the implicit assumption that textures are acquired in the same viewpoint, which is unsuitable for rotation-invariant texture classification. In this paper, rotation-invariant (RI) texture features are developed based on MGF and MRF. A novel algorithm using the neighborhood-oscillating tabu search (NOTS) is proposed to fuse RI MGF and MRF features, compared with the sequential forward floating selection method. Experimental results indicate that the fused RI MGF/MRF features achieved by NOTS have much higher discrimination than pure features in terms of classification accuracy

Power allocation in multi-user wireless relay networks through bargaining

Abstract-In this paper, we consider a multi-user single-relay wireless network, where the relay facilitates transmissions of the users' signals to the destination. We study the relay power allocation among the users, and use bargaining theory to model the negotiation among the users on relay power allocation. By assigning a bargaining power to each user to indicate its transmission priority, we propose an asymmetric Nash bargaining solution (NBS)-based relay power allocation scheme. We also propose a distributed implementation for this solution, where each user only requires its local channel state information (CSI). We analytically investigate the impact of the bargaining powers on the relay power allocation and show that via proper selection of the bargaining powers, the proposed power allocation can achieve a balance between the network sum-rate and the user fairness. Then we generalize the NBS-based power allocation and its distributed implementation to multi-user multi-relay networks. Simulation results are shown to compare the proposed power allocation with sum-rate-optimal power allocation and even power allocation. The impact of the bargaining powers on the power allocation is also demonstrated via simulations

Multi-Feature Aggregation for Semantic Segmentation of an Urban Scene Point Cloud

With the rapid development of cities, semantic segmentation of urban scenes, as an important and effective imaging method, can accurately obtain the distribution information of typical urban ground features, reflecting the development scale and the level of greenery in the cities. There are some challenging problems in the semantic segmentation of point clouds in urban scenes, including different scales, imbalanced class distribution, and missing data caused by occlusion. Based on the point cloud semantic segmentation network RandLA-Net, we propose the semantic segmentation networks RandLA-Net++ and RandLA-Net3+. The RandLA-Net++ network is a deep fusion of the shallow and deep features of the point clouds, and a series of nested dense skip connections is used between the encoder and decoder. RandLA-Net3+ is based on the multi-scale connection between the encoder and decoder; it also connects internally within the decoder to capture fine-grained details and coarse-grained semantic information at a full scale. We also propose incorporating dilated convolution to increase the receptive field and compare the improvement effect of different loss functions on sample class imbalance. After verification and analysis of our labeled urban scene LiDAR point cloud dataset—called NJSeg-3D—the mIoU of the RandLA-Net++ and RandLA-Net3+ networks is 3.4% and 3.2% higher, respectively, than the benchmark network RandLA-Net

Use of GAN to Help Networks to Detect Urban Change Accurately

Mastering urban change information is of great importance and significance in practical areas such as urban development planning, land management, and vegetation cover. At present, high-resolution remote sensing images and deep learning techniques have been widely used in the detection of urban information changes. However, most of the existing change detection networks are Siamese networks based on encoder–decoder architectures, which tend to ignore the pixel-to-pixel relationships and affect the change detection results. To solve this problem, we introduced a generative adversarial network (GAN). The change detection network based on the encoder–decoder architecture was used as the generator of the GAN, and the Jensen-Shannon(JS) scatter in the GAN model was replaced by the Wasserstein distance. An urban scene change detection dataset named XI’AN-CDD was produced to verify the effectiveness of the algorithm. Compared with the baseline model of the change detection network, our generator outperformed it significantly and had higher feature integrity. When the GAN was added, the detected feature integrity was better, and the F1-score increased by 4.4%

Preparation of Magnetic Nano-Catalyst Containing Schiff Base Unit and Its Application in the Chemical Fixation of CO₂ into Cyclic Carbonates

The development of a catalyst for the conversion of CO2 and epoxides to the corresponding cyclic carbonates is still a very attractive topic. Magnetic nano-catalysts are widely used in various organic reactions due to their magnetic separation and recycling properties. Here, a magnetic nano-catalyst containing a Schiff base unit was designed, synthesized and used as a heterogeneous catalyst to catalyze CO2 and epoxides to form cyclic carbonates without solvents and co-catalysts. The catalyst was characterized using Fourier transform infrared (FTIR), X-ray diffraction (XRD), thermogravimetric (TG), VSM, SEM, TEM and BET. The results show that the magnetic nano-catalyst containing the Schiff base unit has a high activity in the solvent-free cycloaddition reaction of CO2 with epoxide under mild conditions, and is easily separated from the reaction mixture driven by external magnetic force. The recovered catalyst maintains a high performance after five cycles

Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City

It is of great significance for the sustainable development of steel cities to explore the relationship between the spatial pattern change in steel plants and land cover change during the transformation of steel cities. To address the issue of unsatisfactory results for segmenting steel plants based on high-resolution remote sensing images, due to insufficient sample datasets and task complexity, we proposed a steel plant segmentation strategy that combines high-resolution remote sensing images, POI data, and OSM data. Additionally, we discussed the effect of POI data and OSM data on steel plant segmentation, analyzing the spatial pattern change in steel plants in Tangshan City during 2017–2022 and its relationship with land cover change. The results demonstrate that: (1) The proposed strategy can significantly improve the accuracy of steel plant segmentation. The introduction of POI data can significantly improve the precision of steel plant segmentation, however, it will to some extent reduce the recall of steel plant segmentation, and this phenomenon weakens as the distance threshold increases. The introduction of OSM data can effectively improve the effectiveness of steel plant segmentation, however, it has significant limitations. (2) During 2017–2022, the spatial distribution center of steel plants in Tangshan City moved obviously to the southeast, and the positive change in steel plants was mainly concentrated in the coastal regions of southern Tangshan City, while the negative change in steel plants was mainly concentrated in central Tangshan City. (3) There is a relatively strong spatial correlation between the positive change in steel plants and the transition from vegetation to built area, as well as the transition from cropland to built area

Study on the Coefficient of Apparent Shear Stress along Lines Dividing a Compound Cross-Section

A compound channel’s discharge capacity and boundary shear force can be predicted as a sum of the discharge capacity of different sub-regions once the apparent shear stress of the dividing line is reasonably quantified. The apparent shear stress was usually expressed as a coefficient multiplied by the difference between two squared velocities of two adjacent regions. This study investigated the range of the coefficient values and their influencing factors. Firstly, the optimal values of the coefficient were obtained based on experimental data. Then, comparisons between the optimal values and several parameters used in quantifying the apparent shear stress were conducted. The results show that the coefficient is mainly related to a morphological parameter of the floodplain and the ratio of resistance coefficients between the floodplain and the main channel. An empirical formula to calculate the coefficient was developed and introduced to calculate the flow discharge and boundary shear stress. Experimental data, including 142 sets of test data of symmetric-floodplain cases and 104 sets of one-floodplain cases, have been used to examine the prediction accuracy of discharges and boundary shear stress. For all these tests, the ranges of water depth of the main channel and the total width of the compound cross-section are about 0.05~0.30 m and 0.3~10 m, respectively; the Q range and the range of Froude numbers of the main channel flow are about 0.0033~1.11 m3/s and 0.3~2.3, respectively. Comparison with other methods and experimental data from both rigid and erodible compound channels indicated that the proposed method not only provided acceptable accuracy for the computation of discharge capacity and boundary shear stress of compound channels in labs but also gave insights for calculating discharge capacity in natural compound channels