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This letter reports a novel method for fabricating Schottky/ohmic drain for GaN high electron mobility transistors (HEMTs). Without secondary photolithography or secondary metal evaporation, the Schottky/ohmic drain can be obtained by adjusting the direction of the electron beam evaporation. Besides, different metal/semiconductor interfaces can be simultaneously formed at this state, which are beneficial to enhance the shielding effect of the rough ohmic-drain metal morphology and modulate the electric field distribution on the drain side. By using Schottky/ohmic drain technology, the average breakdown voltage calculated from 50 devices each is improved from 895 V (ohmic drain device) to 1205 V for $\text{L}_{\text {GD}} = 18\,\,\mu \text{m}$ , indicating enhanced off-state reliability characteristics for GaN HEMTs. [ABSTRACT FROM AUTHOR]

Sonneratia have posed a threat to native mangrove species in Hong Kong. Early detection of individual Sonneratia when they are introduced and naturalized before invasion is essential for native mangrove species protection, especially for Sonneratia with a strong ability of propagation. This letter aims to provide an effective way to the accurate detection of individual Sonneratia. Specifically, using very high spatial resolution remotely sensed data, we adapt the RetinaNet, incorporating multiscale features for sapling detection and convolutional neural networks for detecting the Sonneratia distributed scatteredly among native species. The Sonneratia were detected with a higher mean average precision (mAP) 0.50 of 0.3891 with a precision of 0.5465 than that from the deformable part model. In addition, 3678 Sonneratia were detected at early stage. This letter can support the government for mangrove forest management and offer a scientific guidance for adequate response to the species invasion, like annual removal of Sonneratia, and then reduce the consumption of labor and time over a large scale. In addition, it can provide a quantitative survey for Sonneratia management. [ABSTRACT FROM AUTHOR]

Classification of species at the individual tree level would be beneficial to many applications including forest landscape visualization, forest management, and biodiversity monitoring. This article develops a patch-based classification algorithm of individual tree species based on convolutional neural network. The individual trees are first detected using the local maximum method from the canopy height model, as derived from light detection and ranging (LiDAR) data. The detected individual trees are then cropped into patches for classification based on the tree apexes, and three spatial scale image patches are chosen for analysis and discussion. A modified ResNet50 deep network is further employed for the cropped individual tree patches classification. The patch-based method accounts for the contexture information of a tree and does not require the feature selection or the feature reduction processes. About 1388 training samples including Ficus microcarpa Linn. f., Delonix regia, Chorisia speciosa A.St.-Hil., Dimocarpus longan Lour., Musa nana Lour., Carica papaya, and Others (the other tree species except the above six) were collected from both field work and visual interpretation. Aerial images, LiDAR data, and Worldview images were used for the tree species classification. For 362 test tree samples, the results of patch size 64 achieve the best accuracies, and the proposed method outperforms the traditional machine learning method with the overall accuracy of 89.06% + 0.58% using aerial images only. Transferability Study to the Luhu Park also indicated the feasibility of our method. While challenges in individual tree detection and multisource data fusion remain, the solution shows the potential in characterizing tree species at the individual tree level using remote sensing data. [ABSTRACT FROM AUTHOR]

Incorporating optical and polarimetric synthetic-aperture radar (SAR) data to estimate impervious surface is useful but challenging due to their different geometric imaging mechanism and the high diversity of urban land covers. The recent development of deep convolutional networks (DCN) opens a promising opportunity by automatically extracting the deep features from both data sets. In this study, a small-patched DCN (SDCN) was designed to estimate the impervious surface from optical and SAR data. Benchmark methods, e.g., GoogLeNet, VGG16, ResNet50, and the support vector machine were employed for comparison. Two study sites in the most complex metropolitan of China, the Guangdong-Hong Kong-Macau Greater Bay Area, were selected to assess the proposed method. Experimental results indicated the effectiveness of proposed SDCN with a better accuracy outperforming other benchmark methods. Furthermore, we found that 60%–80% of training samples performed comparably with the whole training set, indicating that a large number of training samples may not be necessary in all cases, depending on the settings of some factors (e.g., number of epochs). Generally, SDCN appears more suitable than other methods in terms of combining the optical and SAR data and improved the accuracy of estimating impervious surface. [ABSTRACT FROM AUTHOR]

Numerous studies on environmental modeling and ecological process lay emphasis on the fundamental information of the impervious surface area (ISA). However, accurate ISA extraction from high-resolution satellite images remains challenging due to both the high diversity of land covers and shadow effects from tall buildings and trees. To address the problem, a discriminative Optical-SAR-LiDAR dictionary sparse representation classification (OSLD-SRC) method was proposed using high-resolution WorldView-2, GeoEye-1, TerraSAR-X, and airborne LiDAR data. First, it used multisource data and fuzzy samples by low-pass filtering (LPF) to solve the problem of roads and buildings misclassification; second, it learned the Optical-SAR-LiDAR dictionary for nonshadow and shadow classes, and then used discriminative sparse coding method for classification to reduce the shadow effects and improve the ISA extraction accuracy. Experimental results demonstrated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Sending broadcast messages is an efficient means of communicating in emergency situations, especially during serious disasters. This paper focuses on the digital audio broadcast (DAB) system. First, several new techniques are reported to enhance the emergency warning message (EWM) receiving and display functionalities for both personal and public DAB terminals. Then, a novel bidirectional communication method between two DAB transmitters is proposed, to enable the communication to and from the disaster area and the outside. Experiments show that the EWM can be correctly sent to the intended DAB terminals within 0.2–3 s, and the existing public light emitting diode screens can be expanded as DAB EWM terminals without affecting their normal functions. The bidirectional multimedia communication between two DAB transmitters is also successfully demonstrated. The proposed techniques expand upon the DAB’s existing functionality, and can benefit more people during emergencies. [ABSTRACT FROM AUTHOR]

The estimation of chlorophyll-a (chl-a) concentration remains a great challenge in turbid waters due to their complex optical conditions. To improve chl-a estimation, this study aims to determine whether combined use of polarimetric synthetic-aperture radar (SAR) data has potential for improving the chl-a estimation from hyperspectral sensing reflectance for turbid waters such as those found in Lake Taihu, China. In situ measurements of hyperspectral reflectance data and water samples were collected over the lake corresponding to ENVISAT ASAR data. Semiempirical (two-band and three-band models) and empirical [multiple linear regression (MLR) and multilayer perceptron network (MLP)] models are compared to estimate the chl-a concentration from in situ hyperspectral reflectance and SAR data. The results show that there is a general underestimation of chl-a for concentrations higher than 26 ug/L, which is probably caused by the large spatial variation of chl-a in the study area. The results also demonstrate that the MLR model performs in a more stable manner than the MLP network does, while MLP underestimates low and high areas of chl-a concentrations in the lake. On the other hand, due to the availability of one scenic SAR data on the same day, our results show that the additional use of SAR data improved chl-a estimation very slightly in this case study, although the performance of vertical/vertical polarization SAR data was better than that of horizontal/horizontal polarization data in chl-a estimation. Potential future work in this subject could explore other measures of mutual information between SAR and hyperspectral optical data beyond the correlation and regression techniques described. Therefore, it is still necessary to apply more SAR data in varied turbid waters in the near future to determine how SAR data can be useful in the improvement of chl-a estimation. [ABSTRACT FROM PUBLISHER]

Numerous studies have demonstrated the benefits of fusing optical and synthetic aperture radar (SAR) data for urban impervious surfaces estimation. Given the increasing availability of SAR satellite data, the synergized use of optical and SAR data is increasingly important. However, previous studies have focused primarily on a single scale. This study aims to evaluate the impervious surfaces estimation using different combinations of optical and SAR data, as well as to quantify the scale effects of combining optical and SAR data under various resolutions from 30 to 1050 m. Six combinations of optical and SAR data in three cities, Shenzhen, Mumbai, and São Paulo, were compared. Experimental results illustrated the effectiveness of using texture and SAR data in improving impervious surface estimation with producer's accuracy improvement of 5%, 0%, and 4% in Shenzhen, Mumbai, and São Paulo at 30 m. The correlation coefficient assessment between the estimated and referenced impervious surfaces at various scales demonstrated that TerraSAR-X data generally outperformed ENVISAT advanced SAR of impervious surface estimation. Estimation accuracy of impervious surfaces in Shenzhen had an improvement of 4% to 5%. Moreover, the texture information of SAR data was observed to provide positive contributions to the impervious surfaces estimation in three study cases at different scales. [ABSTRACT FROM PUBLISHER]

Multisource remote sensing data have been widely used to improve land-cover classifications. The combination of synthetic aperture radar (SAR) and optical imagery can detect different land-cover types, and the use of genetic algorithms (GAs) and support vector machines (SVMs) can lead to improved classifications. Moreover, SVM kernel parameters and feature selection affect the classification accuracy. Thus, a GA was implemented for feature selection and parameter optimization. In this letter, a GA-SVM algorithm was proposed as a method of classifying multifrequency RADARSAT-2 (RS2) SAR images and Thaichote (THEOS) multispectral images. The results of the GA-SVM algorithm were compared with those of the grid search algorithm, a traditional method of parameter searching. The results showed that the GA-SVM algorithm outperformed the grid search approach and provided higher classification accuracy using fewer input features. The images obtained by fusing RS2 data and THEOS data provided high classification accuracy at over 95%. The results showed improved classification accuracy and demonstrated the advantages of using the GA-SVM algorithm, which provided the best accuracy using fewer features. [ABSTRACT FROM PUBLISHER]

This paper describes the design, development and testing of an AR system that was developed for aerospace and ground vehicles to meet stringent accuracy and robustness requirements. The system uses an optical see-through HMD, and thus requires extremely low latency, high tracking accuracy and precision alignment and calibration of all subsystems in order to avoid mis-registration and “swim”. The paper focuses on the optical/inertial hybrid tracking system and describes novel solutions to the challenges with the optics, algorithms, synchronization, and alignment with the vehicle and HMD systems. Tracker accuracy is presented with simulation results to predict the registration accuracy. A car test is used to create a through-the-eyepiece video demonstrating well-registered augmentations of the road and nearby structures while driving. Finally, a detailed covariance analysis of AR registration error is derived. [ABSTRACT FROM AUTHOR]