All-weather Operation Research Articles

An RD-Domain Virtual Aperture Extension Method for Shipborne HFSWR

High-frequency surface wave radar (HFSWR) is widely used for detecting sea surface or low-altitude targets due to its all-weather operation and over-the-horizon detection capability. To further enhance the maneuverability and detection range of HFSWR, shipborne HFSWR has been developed. However, compared to shore-based platforms, shipborne platforms face challenges such as a small array aperture and reduced Direction of Arrival (DOA) estimation performance due to their limited size. The traditional time–domain virtual aperture extension method, based on the principle of space-time equivalence, aims to improve the array aperture but has limitations when used for HFSWR background or multiple targets with different speeds. To address these issues, this paper proposes a range-Doppler domain (RD-domain) virtual aperture extension method for the uniform linear array, based on the uniform motion model. The contributions of this work include (1) a continuous motion model for shipborne HFSWR, (2) a virtual aperture processing flowchart for shipborne HFSWR, and (3) an RD-domain aperture extension method suitable for HFSWR background or multiple targets with varying speeds. Through simulation and experimental data, we validate the proposed method and analyze its performance.

Three-dimensional deformation monitoring of landslides based on combination of two-track InSAR observations and pixel-level surface-parallel flow model

ABSTRACT Landslides are characterized by intricate formation mechanisms and multiple triggering factors, posing serious threats to human life and property. Interferometric synthetic aperture radar (InSAR) has been extensively applied in landslide monitoring due to its all-weather operation, high precision, and wide spatial coverage. However, one-dimensional line-of-sight (LOS) deformation limits the ability to measure actual landslide displacement. Presently, many regions without the latest descending data, but they are in the overlapping position of two adjacent latest ascending track. Compared to using a single track, the method measuring three-dimensional deformation of landslides, combined with two ascending track InSAR datasets and pixel-level surface-parallel flow (PSPF) assumption, is presented. Based on the small baseline subset (SBAS) InSAR, the LOS deformation in two observations in the middle reaches of the Qingjiang River in China was measured. With pixel-level surface-parallel flow model, the three-dimensional deformation was obtained. It was further converted into the slope surface coordinate system, thus determining the direction of landslide movement. The findings effectively elucidate the primary displacement mode of landslides, with deformation rates ranging from −80 to 20 mm yr − 1 , which are significantly influenced by the slope angle and slope aspect. It is highly important to remotely identify the movement direction and analyse the failure mode of landslides.

The Identification and Influence Factor Analysis of Landslides Using SBAS-InSAR Technique: A Case Study of Hongya Village, China

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loess, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation.

Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud

Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set.

An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications.

Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods.

Electrolyte Chemistry toward Ultrawide-Temperature (-25 to 75 °C) Sodium-Ion Batteries Achieved by Phosphorus/Silicon-Synergistic Interphase Manipulation.

All-weather operation is considered an ultimate pursuit of the practical development of sodium-ion batteries (SIBs), however, blocked by a lack of suitable electrolytes at present. Herein, by introducing synergistic manipulation mechanisms driven by phosphorus/silicon involvement, the compact electrode/electrolyte interphases are endowed with improved interfacial Na-ion transport kinetics and desirable structural/thermal stability. Therefore, the modified carbonate-based electrolyte successfully enables all-weather adaptability for long-term operation over a wide temperature range. As a verification, the half-cells using the designed electrolyte operate stably over a temperature range of -25 to 75 °C, accompanied by a capacity retention rate exceeding 70% even after 1700 cycles at 60 °C. More importantly, the full cells assembled with Na3V2(PO4)2O2F cathode and hard carbon anode also have excellent cycling stability, exceeding 500 and 1000 cycles at -25 to 50 °C and superb temperature adaptability during all-weather dynamic testing with continuous temperature change. In short, this work proposes an advanced interfacial regulation strategy targeted at the all-climate SIB operation, which is of good practicability and reference significance.

Model Predictive Control-Based Dual-Mode Operation of an Energy-Stored Quasi-Z-Source Photovoltaic Power System

The energy-stored quasi-Z-source inverter (ES-qZSI) has attracted much attention for photovoltaic (PV) power generations, due to its capability to stabilize the PV power fluctuations with simple structure and other advantages of Z-source inverter. The improved dual-mode (DM) ES-qZSI is able to support all-weather operation even at night or cloudy days when PV power is extremely low. Whereas, the traditional proportional-integral (PI) based control suffers from complicated controller design and poor dynamic response during mode transition, due to two sets of PI control required for the daytime and night operation modes. In order to overcome that, this paper proposes a model predictive control strategy for the DM-ES-qZSI PV power system. The system predictive models in both day and night operating modes are derived. The control strategy is disclosed to ensure high performance of the system, through calling the predictive models and defined cost functions of the two modes within a single control loop. Simulation and experiment are carried out to verify the effectiveness of proposed control strategy.

A novel semi-empirical model for crop leaf area index retrieval using SAR co- and cross-polarizations

The retrieval of continuous leaf area index (LAI) in space and time from remote sensing is beneficial for cropland monitoring and management. Synthetic Aperture Radar (SAR) with the advantages of all-weather operation and fine spatial resolutions has been utilized in various agricultural applications. Although the water cloud model (WCM) has been extensively used for LAI estimation over croplands, it is modified for application to crop canopies with large gaps. The requirement of prior information on soil moisture is also a hinderance for the model application. In this study, WCM is theoretically modified to consider the correlation of active microwave propagation through the canopy in downward and upward directions. Such a modification is particularly important for sparse vegetation with large gaps between crop plants in the early stage of the growing season. A parameter with explicit physical meaning, i.e. the full-vegetation backscattering coefficient, was defined to simplify our model scheme and make the model applicable for the whole growing season. In addition, a physics-based SAR data processing scheme is developed to entangle the influences of LAI and soil moisture on SAR backscatter by taking advantage of the multiple polarizations of RADARSAT-2 (R2) SAR data. In this way, LAI was estimated using modified WCM without the prior knowledge of soil moisture. To evaluate LAI retrieved from the R2 datasets (R2 LAI), ground-based LAI measurements were made at the experimental area of SMAPVEX16-MB in Canada with twenty approximately 800 m х 800 m plots in soybeans and corn. R2 LAI was well correlated to these ground-based LAI (n = 15, R2 = 0.63, RMSE = 0.99 m2·m−2 for soybeans; n = 5, R2 = 0.66, RMSE = 1.20 m2·m−2 for corn). LAI was also retrieved from optical data acquired by Sentinel-2/MSI (S2), denoted as S2 LAI. The R2 LAI and S2 LAI are well correlated and achieved coefficients of determination (R2) of 0.76 and 0.71 and root mean square errors (RMSE) of 1.1 and 1.4 m2·m−2 for soybeans and corn, respectively. The seasonal variations of R2 LAI and S2 LAI are generally similar except at the end of the growing season when S2 LAI is considerably larger than R2 LAI. S2 LAI followed the trend of the reduction in leaf chlorophyll content, while R2 LAI reduced only slightly due to the decrease in leaf water content near the end the growing season. R2 LAI represents the total standing leaf area useful for surface energy balance estimation, while S2 LAI responds to green leaf area useful for crop productivity modeling.

Using Robust Regression to Retrieve Soil Moisture from CyGNSS Data

Accurate global soil moisture (SM) data are crucial for modeling land surface hydrological cycles and monitoring climate change. Spaceborne global navigation satellite system reflectometry (GNSS-R) has attracted extensive attention due to its unique advantages, such as faster revisit time, lower payload costs, and all-weather operation. GNSS signal reflected at L-band also has significant advantages for SM estimation. Usually, SM is estimated based on the sensitivity of GNSS-R reflectivity to SM, but the noise in observations can significantly impact SM estimation results. A new SM retrieval method based on robust regression is proposed to address this issue in this work, and the effects of roughness and vegetation on the effective reflectivity of the Cyclone Global Navigation Satellite System (CyGNSS) are reconsidered. Ancillary data are provided by the SM Active Passive (SMAP) mission. The retrieved results from the training sets and test sets agree well with the referenced SMAP SM data. The correlation coefficient R is 0.93, the root mean square error (RMSE) is 0.058 cm3cm−3, the unbiased RMSE (ubRMSE) is 0.042 cm3cm−3, and the mean absolute error (MAE) is 0.040 cm3cm−3 in the training sets. For the test, the correlation coefficient is 0.91, the RMSE is 0.067 cm3cm−3, the ubRMSE is 0.051 cm3cm−3, and the MAE is 0.044 cm3cm−3. The proposed method has been evaluated using in situ measurements from the SMAP/in situ core validation site; in situ measurements and retrieval results exhibit good consistency with the ubRMSE value below 0.35 cm3cm−3. Moreover, the SM retrieval results using robust regression methods show better performance than CyGNSS official SM products that use linear regression. In addition, the land cover types significantly affect the accuracy of SM retrieval, and the incoherent scattering in densely vegetated areas (tropical forests) usually leads to more errors.

UAV icing: Development of an ice protection system for the propeller of a small UAV

Atmospheric icing, also called in-flight icing, is a common hazard for the operation of uncrewed aerial vehicles (UAVs). With the background of a growing commercial and military market of small and medium-sized drones and the developments in the urban air mobility markets, protecting the propellers of UAVs against icing has become a pivotal technology to unlock the potential of these markets. The propellers and rotors accumulate ice faster than the UAVs’ wings and airframe. This ice accumulation leads to aerodynamic degradation, making the protection of the propeller key for the operation of UAVs in conditions with potential icing. In this work, an electro-thermal ice protection system is developed for a propeller of a small UAV, with a propeller diameter of 53 cm or 21 inch. For the design of the system, the required anti-icing heat fluxes were calculated using icing computational fluid dynamics (CFD) analysis. Carbon fibre-based heating elements were integrated into the structure of the propeller. This propeller was tested in an icing wind tunnel at −5 °C, −10 °C, and −15 °C. The tests were performed with a rotation rate representative for a medium-sized UAV of 4200 rpm. The liquid water content of the wind tunnel was 0.44 g/m3. It prevented the performance penalties from ice accretion at temperatures of −5 °C. At lower temperatures, the propeller IPS could limit the ice accretion on the propeller, thus allowing it to retain its ability to create thrust. The results showed a requirement for a significantly higher heat flux than predicted by the CFD analysis. This work is the first step in developing a mature ice protection system for the propellers and rotors of medium-sized UAVs and to enable all-weather operations.

Spatial-spectral methods of increasing the efficiency of artillery reconnaissance

The author analyzes the evolution of reconnaissance-fire technology based on reconnaissance-strike (RSS) and reconnaissance-fire (RFS) systems and its transition to the ideology of reconnaissance-fire systems (RFS).
 Based on Boyd's cybernetic model, the situational model of RFS combat control and the issue of optimizing it in terms of the "observe" cycle are considered. Practical ways of implementing the developed approach based on multichannel spatial and multispectral processing of intelligence information are substantiated. The optimality of the developed approach is confirmed by the results of a model experiment.
 A methodology for adapting the characteristics of multi-channel processing of location information to a dynamically changing mobile phone environment has been developed. The optimality of the developed approach is confirmed by the results of the model experiment, and the practical feasibility is confirmed by the patent for the invention.The maximum combat effectiveness of the first stage of the Boyd cycle (artillery reconnaissance) – range, accuracy, target channel, immunity - is achieved by the integration of ground-based radar means of reconnaissance of firing positions and UAVs with multispectral surveillance equipment. 
 The practical significance of the obtained results is provided by the independence of targeting accuracy from the target range with simultaneous provision of round-the-clock operation, all-weather operation, interference resistance and potential informativeness of artillery reconnaissance. The attainable gain in artillery reconnaissance effectiveness indicators is 7.2 times the range, and 20 times the target channel. All analytical results are aimed at implementation in the practice of designing the production of equipment for ground and on-board reconnaissance components.

Ultraviolet random access collaborative networking protocol based on time division multiple access

The ultraviolet network, with its advantages of high security, non-line-of-sight communication, and all-weather operation, has broad military application potential. This study proposes an ultraviolet random access collaborative networking protocol based on time division multiple access that combines the benefits of synchronous and asynchronous media access control protocols. Considering the physical layer characteristics, the frame structure of the protocol is constructed based on a cross-layer design. Compared with the time division multiple access protocol, the proposed protocol can achieve higher throughput with a lower delay and packet loss rate by the dynamic timeslot allocation mechanism. Then, the effects of maximum cache capacity and burst traffic on network performance are investigated. Finally, the conducted network experiments prove the cooperative forwarding and random access capabilities of the ultraviolet random access collaborative networking protocol. The proposed protocol provides a reliable and effective method for independent and flexible ultraviolet networks.

Siamese Cross-Domain Tracker Design for Seamless Tracking of Targets in RGB and Thermal Videos

Multimodal (RGB and thermal) applications are swiftly gaining importance in the computer vision community with advancements in self-driving cars, robotics, Internet of Things, and surveillance applications. Both the modalities have complementary performance depending on illumination constraints. Hence, a judicious combination of both modalities will result in robust RGBT systems capable of all-day all-weather applications. Several studies have been proposed in the literature for integrating the multimodal sensor data for object tracking applications. Most of the proposed networks try to delineate the information into modality-specific and modality shared features and attempt to exploit the modality shared features in enhancing the modality specific information. In this work, we propose a novel perspective to this problem using a Siamese inspired network architecture. We design a custom Siamese cross-domain tracker architecture and fuse it with a mean shift tracker to drastically reduce the computational complexity. We also propose a constant false alarm rate inspired coasting architecture to cater for real-time track loss scenarios. The proposed method presents a complete and robust solution for object tracking across domains with seamless track handover for all-day all-weather operation. The algorithm is successfully implemented on a Jetson-Nano, the smallest graphics processing unit (GPU) board offered by NVIDIA Corporation.

GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

Mutual Interference Mitigation of Millimeter-Wave Radar Based on Variational Mode Decomposition and Signal Reconstruction

As an important remote sensing technology, millimeter-wave radar is used for environmental sensing in many fields due to its advantages of all-day, all-weather operation. With the increasing use of radars, inter-radar interference becomes increasingly critical. Severe mutual interference degrades radar signal quality and affects the performance of post-processing, e.g., synthetic aperture radar (SAR) imaging and target tracking. Aiming to deal with mutual interference, we propose an interference mitigation method based on variational mode decomposition (VMD). With the characteristics that the target is a single-frequency sine wave and the interference is a broadband signal, VMD is used for decomposing the radar received signal and separating the target from the interference. Interference mitigation is then implemented in each decomposed mode, and an interference-free signal is obtained through the reconstruction process. Simulation results of multi-target scenarios demonstrate that the proposed method outperforms existing decomposition-based methods. This conclusion is also confirmed by the experimental results on real data.

Research on Simultaneous localization and mapping Algorithm based on Lidar and IMU.

In recent years, the research of autonomous driving and mobile robot technology is a hot research direction. The ability of simultaneous positioning and mapping is an important prerequisite for unmanned systems. Lidar is widely used as the main sensor in SLAM (Simultaneous Localization and Mapping) technology because of its high precision and all-weather operation. The combination of Lidar and IMU (Inertial Measurement Unit) is an effective method to improve overall accuracy. In this paper, multi-line Lidar is used as the main data acquisition sensor, and the data provided by IMU is integrated to study robot positioning and environment modeling. On the one hand, this paper proposes an optimization method of tight coupling of lidar and IMU using factor mapping to optimize the mapping effect. Use the sliding window to limit the number of frames optimized in the factor graph. The edge method is used to ensure that the optimization accuracy is not reduced. The results show that the point plane matching mapping method based on factor graph optimization has a better mapping effect and smaller error. After using sliding window optimization, the speed is improved, which is an important basis for the realization of unmanned systems. On the other hand, on the basis of improving the method of optimizing the mapping using factor mapping, the scanning context loopback detection method is integrated to improve the mapping accuracy. Experiments show that the mapping accuracy is improved and the matching speed between two frames is reduced under loopback mapping. However, it does not affect real-time positioning and mapping, and can meet the requirements of real-time positioning and mapping in practical applications.

A Novel Low-Cost GNSS Solution for the Real-Time Deformation Monitoring of Cable Saddle Pushing: A Case Study of Guojiatuo Suspension Bridge

Extreme loadings, a hostile environment and dangerous operation lead to the unsafe state of bridges under construction, especially large-span bridges. Global Navigation Satellite Systems (GNSS) tend to be the best choice for real-time deformation monitoring due to the significant advantage of automation, continuation, all-weather operation and high precision. Unfortunately, the traditional geodetic GNSS instrument with its high price and large volume is limited in its applications. Hence, we design and develop low-cost GNSS equipment by simplifying the monitoring module. The performance of the proposed solution is evaluated through an experimental dynamic scenario, proving its ability to track abrupt deformation down to 3–5 mm. We take Chongqing Guojiatuo Suspension Bridge in China as a case study. We build a real-time low-cost GNSS monitoring cloud platform. The low-cost bridge GNSS monitoring stations are located at the top of the south and north towers, midspan upstream and downstream respectively and the reference station is located in the stable zone 400 m away from the bridge management buildings. We conducted a detailed experimental assessment of low-cost GNSS on 5 April and a real-time deformation detection experiment of the towers and main cables during the dynamic cable saddle pushing process on 26 February 2022. In the static experiment, the standard deviation of the residual using the multi-GNSS solution is 2 mm in the horizontal direction and 5 mm in the vertical direction. The multi-GNSS solution significantly outperforms the BDS/GPS single system. The dynamic experiment shows that, compared with the movement measured by the robotic total station, the horizontal error of the south tower and north tower measured by low-cost GNSS is below 0.005 m and 0.008 m respectively. This study highlights the potential of low-cost GNSS solutions for Structural Health Monitoring (SHM) applications.

Flight Control Technology Advancements and Challenges for Future Rotorcraft 40th Alexander A. Nikolsky Honorary Lecture

The goal of my 40th Alexander A. Nikolsky Honorary Lecture and journal paper is to highlight the key flight control technology advances of the past 50 years and demonstrate how these advances are being applied and extended to the new family of rotorcraft: modern high-speed military rotorcraft, eVTOL urban air mobility, and advanced air mobility aircraft. The first part of this journal paper reviews flight control technologies drivers that are unique to rotorcraft and highlights key advances of the past 50 years in the areas of handling-qualities requirements (ADS-33), physics-based models, system identification, and flight control. A central theme is the shift from time-domain to frequency-domain based characterization of the closed-loop response and design methods for rotorcraft that have become increasingly dependent on sophisticated feedback control systems to achieve closed-loop stability, disturbance rejection, and most importantly closed-loop handlingqualities response for all-weather operations. Frequency-domain analysis, design, and test methods of the past 50 years are highlighted relating key advances in each discipline and two integrated example success stories. In the second part of this paper, we consider the key challenges, advancements, and needed future research for four new classes of rotorcraft: the military future vertical lift family of high-speed rotorcraft, unmanned autonomous systems/urban air mobility based on fielded conventional helicopters, small electric VTOL unmanned aerial vehicle rotorcraft, and larger eVTOL urban air mobility rotorcraft. The next sections look across the challenges and solution spaces that are common to these four new classes of rotorcraft as a blueprint for needed research advances. Finally, this paper takes a step back and considers the lessons-learned and key takeaways from the author's perspective as a career-long flight control engineer/researcher, Flight Control Technology Group leader, and senior technologist.

Oil Spill Detection with Dual-Polarimetric Sentinel-1 SAR Using Superpixel-Level Image Stretching and Deep Convolutional Neural Network

Synthetic aperture radar (SAR) has been widely applied in oil spill detection on the sea surface due to the advantages of wide area coverage, all-weather operation, and multi-polarization characteristics. Sentinel-1 satellites can provide dual-polarized SAR data, and they have high potential for successful application to oil spill detection. However, the characteristics of the sea surface and oil film on different images are not the same when imaging at different locations and in different conditions, which leads to the inconsistent accuracy of these images with the application of the current oil spill detection methods. In order to avoid the above limitation, we propose an oil spill detection method using image stretching based on superpixels and a convolutional neural network. Experiments were carried out on eight Sentinel-1 dual-pol data, and the optimal superpixel number and image stretching parameters are discussed. Mean intersection over union (MIoU) was used to evaluate classification accuracy. The proposed method could effectively improve the classification accuracy; when the expansion and inhibition coefficients of image stretching were set to 1.6 and 1.2 respectively, the experiments achieved a maximum MIoU of 85.4%, 7.3% higher than that without image stretching.

Revealing the real-time diversity and abundance of small mammals by using an Intelligent Animal Monitoring System (IAMS).

It is challenging to reveal the real-time spatio-temporal change of diversity and abundance of animals in natural systems by using traditional methods. The rapid advancement of new technologies such as the Internet of Things, artificial intelligence, and big-data processing, provide opportunities for developing novel technologies for monitoring biodiversity and population abundance of animals with high efficacy and accuracy. In this study, by using a recently developed Intelligent Animal Monitoring System, named "Vector Intelligent Monitoring System (VIMS)", we investigated the real-time diversity and abundance of small mammals in the Banruosi forest, Dujiangyan region, southwest China. To make a comparison of the VIMS with traditional methods, we also surveyed the diversity and abundance of small mammals using wired live traps. Compared to live traps, the VIMS has several advantages such as automatic data collection, intelligent identification of species, data visualization, whole-day and all-weather operation, little disturbance to animals, real-time monitoring, and is capable of revealing more small mammal species. However, the VIMS also has several disadvantages over live traps such as lower trapping efficiency and being more expensive than live traps. Our results suggest that the VIMS can be a complementary method to traditional ones in monitoring the real-time spatio-temporal change of diversity and abundance of small mammals (especially rare species). In addition, the VIMS is useful in monitoring other small animals like small carnivores, birds, amphibians, and reptiles.