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Remote sensing (RS) image segmentation is an essential step in geographic object-based image analysis (GEOBIA) to ultimately derive “meaningful objects”. While many segmentation methods exist, most of them are not efficient for large data sets. Thus, the goal of this research is to develop an efficient parallel multi-scale segmentation method for RS imagery by combining graph theory and the fractal net evolution approach (FNEA). Specifically, a minimum spanning tree (MST) algorithm in graph theory is proposed to be combined with a minimum heterogeneity rule (MHR) algorithm that is used in FNEA. The MST algorithm is used for the initial segmentation while the MHR algorithm is used for object merging. An efficient implementation of the segmentation strategy is presented using data partition and the “reverse searching-forward processing” chain based on message passing interface (MPI) parallel technology. Segmentation results of the proposed method using images from multiple sensors (airborne, SPECIM AISA EAGLE II, WorldView-2, RADARSAT-2) and different selected landscapes (residential/industrial, residential/agriculture) covering four test sites indicated its efficiency in accuracy and speed. We conclude that the proposed method is applicable and efficient for the segmentation of a variety of RS imagery (airborne optical, satellite optical, SAR, high-spectral), while the accuracy is comparable with that of the FNEA method. [ABSTRACT FROM AUTHOR]

The methods of chemical vapor deposition (CVD) and seed-mediated growth were used to obtain graphene and gold nanorods (GNRs), respectively. We fabricate graphene @ gold nanorods (G@GNRs) nanocomposites by successively using dropping and transferring methods Through SEM, Raman spectra and TEM analysis, the number of graphene layers is 6–7. The diameter of gold nanorods (GNRs) is about 10 nm and the average aspect ratio is 6.5. In addition, we systematically investigate their nonlinear optical responses by using open-aperture Z -scan technique. In contrast with graphene and GNRs, the G@GNRs nanocomposites exhibit excellent nonlinear optical response with a modulation depth of about 51% and a saturable intensity of about 6.23 GW/cm 2 . The results suggest that the G@GNRs nanocomposites could potentially be used as an optical modulator in pulsed laser generation. [ABSTRACT FROM AUTHOR]

This paper presents a new object-oriented land cover classification method that integrates raster analysis and vector analysis. The method adopts an improved colour structure code (CSC) for segmentation and support vector machine (SVM) for classification using high resolution (HR) QuickBird data. It combines the advantages of digital image processing (efficient improved CSC segmentation), geographical information systems (GIS) (vector-based feature selection), and data mining (intelligent SVM classification) to interpret images from pixels to objects and thematic information. The improved CSC segmentation not only achieves robust and accurate results but also combines boundary information that the traditional CSC algorithm does not consider. The SVM used for classification has the advantages of solving sparse sampling, nonlinear, high-dimensional and global optimum problems, compared with other classifiers. The results demonstrate that the new object-oriented classification method significantly outperforms some other objected-oriented classification methods such as the objected-oriented method based on traditional CSC and SVM, and perfect classification results are obtained from the classification processing, including not only the classification method, but also preprocessing, sample selection and post-processing. [ABSTRACT FROM AUTHOR]

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas. In this study, Advanced Synthetic Aperture Radar (ASAR) observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin, Northwest China. A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter (EnKF), the forward radiative transfer model, crop model, and the Distributed Hydrology-Soil-Vegetation Model (DHSVM) was developed. The crop model, as a semi-empirical model, was used to estimate the surface backscattering of vegetated areas. The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape. Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June 20 to July 15, 2008. The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model. Compared with the simulation and in situ observations, the assimilated results were significantly improved in the surface layer and root layer, and the soil moisture varied slightly in the deep layer. Additionally, EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data. Moreover, to improve the assimilation results, further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed, also improving estimation accuracy of model operator is important. [ABSTRACT FROM AUTHOR]

• A tree species classification method based on three-dimensional deep learning is proposed. • The network we named LayerNet can directly extract high-dimensional features from point sets. • The network relies on the global features of the tree that incorporate layered local information. Accurate tree species identification is essential for ecological evaluation and other forest applications. In this paper, we proposed a point-based deep neural network called LayerNet. For light detection and ranging (LiDAR) data in forest regions, the network can divide multiple overlapping layers in Euclidean space to obtain the local three-dimensional (3D) structural features of the tree. The features of all layers are aggregated, and the global feature is obtained by convolution to classify the tree species. To validate the proposed framework, multiple experiments, including airborne and ground-based LiDAR datasets, are conducted and compared with several existing tree species classification algorithms. The test results show that LayerNet can directly use 3D data to accurately classify tree species, with the highest classification accuracy of 92.5%. Also, the results of comparative experiments demonstrate that the proposed framework has obvious advantages in classification accuracy and provides an effective solution for tree species classification tasks. [ABSTRACT FROM AUTHOR]

• First, the main innovation of this study is that we achieved high-energy mode-locked pulse in a mode-locked Er-doped fiber laser (EDFL) with a Bi 2 Se 3 /mica saturable absorber (SA), and the average output power is 185.3 mW, single pulse energy is 171.3 nJ. To the best of our knowledge, for all SA based mode-locked EDFL, our 185.3 mW is the largest value of average output power, and 171.3 nJ is the largest value of single pulse energy. We've made a significant improvement in the output energy performance. • Second, to our best knowledge, this is the first time that dissipative soliton resonance (DSR) pulse was generated in a Bi 2 Se 3 SA based mode-locked fiber laser. Recent years, DSR mode-locked fiber lasers have aroused the attention because of the theoretically infinite pulse energy of the DSR pulse. Our results suggested that the Bi 2 Se 3 /mica SAs could be used as excellent photonics for generation of high-energy pulses in 2D materials-based fiber lasers. With chemical vapor deposition (CVD) method, Bismuth Selenide (Bi 2 Se 3)/mica was synthesized and developed into a saturable absorber (SA). This type of SA has a large optical threshold and outstanding thermal stability, offering the possibility to work in a high-energy pulsed fiber laser. While the Bi 2 Se 3 /mica SA inserted into an Er-doped fiber laser, the large-energy mode-locked pulse was achieved easily. The maximum average output power and pulse energy were 185.3 mW and 171.3 nJ, respectively. Our results proved that Bi 2 Se 3 /mica with a large damage threshold could be an excellent material for high-energy pulsed fiber lasers. [ABSTRACT FROM AUTHOR]

A highly sensitive Au-graphene structure D-type fiber surface plasmon resonance biosensor is presented in this study to specifically detect biomolecules. The method of growing graphene is employed directly on the copper, and then a gold film of optimum thickness is sputtered, and the copper foil is etched to obtain the structure. This method makes the contact closer between the gold layer and the graphene layer to improve surface plasmon resonance performance. The performance of this type of surface plasmon resonance (SPR) sensor has been previously verified both theoretically and experimentally. With the proposed Au-graphene structure D-type fiber biosensor, the SPR behaviors are obtained and discussed. In the detection of ethanol solution, a red shift of 40 nm is found between the refractive index of 1.3330 and 1.3657. By calculation, the sensitivity of the sensor we designed is 1223 nm/RIU. Besides, the proposed sensor can detect the nucleotide bonding between the double-stranded DNA helix structures. Thus, our sensors can distinguish between mismatched DNA sequences. [ABSTRACT FROM AUTHOR]