With the advent of high-resolution remote sensing images, automatic building extraction methods play a more important role in rapidly acquiring information about large-scale buildings. Although advanced building extraction methods have been introduced to improve building extraction results, these methods involve complex processing and high-computation times. We put forward an effective method to extract building information, based on a proposed spectral building index. The basic idea of the spectral building index is to generate an optimized index based on the computation and analysis of spectral bands, which are beneficial for image enhancement for buildings in images. Aiming at the band number of the multispectral satellite images in high-resolution remote sensing images, we propose two spectral indices for building extraction, including the normalized spectral building index (NSBI) and the difference spectral building index (DSBI). Considering the current spectral band number of high-resolution satellite images, NSBI is suited for satellite images with eight spectral bands, whereas DSBI is suited for satellite images with four spectral bands. The proposed method is validated on various high-resolution images including WorldView-2, GF-1, GF-2, and QuickBird images with 13 experiment datasets, as well as a detailed comparison to the state-of-the-art methods, such as the morphological building index, nonhomogeneous feature difference, and building condition index. The experimental results reveal that the proposed method can achieve promising results for different building conditions, such as regular and irregular building shapes and concrete and metal roofing materials. The average overall accuracy was over 85% with low-time consumption (<1 s).
The Overhauser magnetometer, with its unique set of advantages, such as low power consumption, high precision and
fast recording ability has been widely used in geophysical mineral and oil exploration, archeology, environmental survey,
ordnance and weapons detection (UXO) and other earth science applications. Compared with the traditional proton
magnetometer, which suffers from high power consumption and low precision, the Overhauser magnetometer excite the
free radical solution in a cavity with RF signal to enhance nuclear magnetic resonance (NMR). Thus, RF resonator plays
a crucial role in reducing power consumption and improving the accuracy of Overhauser magnetometer. There are a wide
variety of resonators, but only two of them are chosen for Overhauser magnetometer: birdcage coil and coaxial resonator.
In order to get the best RF cavity for Overhauser magnetometer sensor, both resonators are investigated here. Firstly,
parameters of two RF resonators are calculated theoretically and simulated with Ansoft HFSS. The results indicate that
birdcage coil is characterized by linear polarization while coaxial resonator is characterized by circular polarization.
Besides, all RF fields are limited inside of the coaxial resonator while distributed both inside and outside of the birdcage
coil. Then, the two resonators are practically manufactured based on the theoretical design. And the S-parameter and
Smith chart of these resonators are measured with Agilent 8712ES RF network analyzer. The measured results indicate
that the coaxial resonator has a much higher Q value(875) than the birdcage coil(70). All these results reveal a better
performance for coaxial resonator. Finally, field experimental shows 0.074nT sensitivity for Overhauser magnetometer
with coaxial resonator.
In VHF pulse Ground Penetrating Radar(GPR) system, the echo pass through the antenna and transmission line circuit, then reach the GPR receiver. Thus the reflection coefficient at the receiver sampling gate interface, which is at the end of the transmission line, is different from the real reflection coefficient of the media at the antenna interface, which could cause the GPR receiving error. The pulse GPR receiver is a wideband system that can't be simply described as traditional narrowband transmission line model. Since the GPR transmission circuit is a linear system, the linear transformation method could be used to analyze the characteristic of the GPR receiving system. A GPR receiver calibration method based on transmission line theory is proposed in this paper, which analyzes the relationship between the reflection coefficients of theory calculation at antenna interface and the measuring data by network analyzer at the sampling gate interface. Then the least square method is introduced to calibrate the transfer function of the GPR receiver transmission circuit. This calibration method can be useful in media quantitative inversion by GPR. When the reflection coefficient at the sampling gate is obtained, the real reflection coefficient of the media at the antenna interface can be easily determined.
KEYWORDS: Signal to noise ratio, Magnetometers, Magnetism, Field effect transistors, Amplifiers, Sensors, Bandpass filters, Oscillators, Clocks, Digital electronics
Overhauser magnetometer is a kind of high-precision devices for magnetostatic field measurement. It is widely used in geological survey, earth field variations, UXO detection etc. However, the original Overhauser magnetometer JOM-2 shows great shortcomings of low signal to noise ratio (SNR) and high power consumption, which directly affect the performance of the device. In order to increase the sensitivity and reduce power consumption, we present an improved Overhauser magnetometer. Firstly, compared with the original power board which suffers from heavy noise for improper EMC design, an improved power broad with 20mV peak to peak noise is presented in this paper. Then, the junction field effect transistor (JFET) is used as pre-amplifier in our new design, to overcome the higher current noise produced by the original instrumentation amplifier. By adjusting the parameters carefully low noise factor down to 0.5 dB can be obtained. Finally, the new architecture of ARM + CPLD is adopted to replace the original one with DSP+CPLD. So lower power consumption and greater flash memory can be realized. With these measures, an improved Overhauser magnetometer with higher sensitivity and lower power consumption is design here. The experimental results indicate that the sensitivity of the improved Overhauser magnetometer is 0.071nT, which confirms that the new magnetometer is sensitive to earth field measurement.
With the rapid development of remote sensing technology, the spatial resolution and temporal resolution of satellite imagery also have a huge increase. Meanwhile, High-spatial-resolution images are becoming increasingly popular for commercial applications. The remote sensing image technology has broad application prospects in intelligent traffic. Compared with traditional traffic information collection methods, vehicle information extraction using high-resolution remote sensing image has the advantages of high resolution and wide coverage. This has great guiding significance to urban planning, transportation management, travel route choice and so on. Firstly, this paper preprocessed the acquired high-resolution multi-spectral and panchromatic remote sensing images. After that, on the one hand, in order to get the optimal thresholding for image segmentation, histogram equalization and linear enhancement technologies were applied into the preprocessing results. On the other hand, considering distribution characteristics of road, the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used to suppress water and vegetation information of preprocessing results. Then, the above two processing result were combined. Finally, the geometric characteristics were used to completed road information extraction. The road vector extracted was used to limit the target vehicle area. Target vehicle extraction was divided into bright vehicles extraction and dark vehicles extraction. Eventually, the extraction results of the two kinds of vehicles were combined to get the final results. The experiment results demonstrated that the proposed algorithm has a high precision for the vehicle information extraction for different high resolution remote sensing images. Among these results, the average fault detection rate was about 5.36%, the average residual rate was about 13.60% and the average accuracy was approximately 91.26%.
Building extraction currently is important in the application of high-resolution remote sensing imagery. At present, quite a few algorithms are available for detecting building information, however, most of them still have some obvious disadvantages, such as the ignorance of spectral information, the contradiction between extraction rate and extraction accuracy. The purpose of this research is to develop an effective method to detect building information for Chinese GF-1 data. Firstly, the image preprocessing technique is used to normalize the image and image enhancement is used to highlight the useful information in the image. Secondly, multi-spectral information is analyzed. Subsequently, an improved morphological building index (IMBI) based on remote sensing imagery is proposed to get the candidate building objects. Furthermore, in order to refine building objects and further remove false objects, the post-processing (e.g., the shape features, the vegetation index and the water index) is employed. To validate the effectiveness of the proposed algorithm, the omission errors (OE), commission errors (CE), the overall accuracy (OA) and Kappa are used at final. The proposed method can not only effectively use spectral information and other basic features, but also avoid extracting excessive interference details from high-resolution remote sensing images. Compared to the original MBI algorithm, the proposed method reduces the OE by 33.14% .At the same time, the Kappa increase by 16.09%. In experiments, IMBI achieved satisfactory results and outperformed other algorithms in terms of both accuracies and visual inspection
Snow parameters are important physical quantities of climatology and hydrology research, improving the accuracy of snow parameters is important for climatology, hydrology and disaster prevention and reduction. The western Jilin Province of China has obvious salinization problem. Meanwhile, it belongs to a typical snow-covered area. In this paper, the western Jilin Province is selected as the study area and the main research focuses on analyzing the snow cover conditions. The FY3B-MWRI passive microwave remote sensing data from year 2011 to 2016 are selected as experimental data. Compared with optical remote sensing data, using MWRI data can better obtain snow information, and it is also the preliminary work to retrieve snow depth and snow water equivalent. Furthermore, a new decision tree algorithm for snow cover identification was built to distinguish different snow cover conditions. Compared with the existing three algorithms reported in other literatures, the proposed algorithm improves the identification accuracy of snow cover up to 95.06%. While the accuracy for Singh’s algorithm, Pan’s algorithm and Li’s algorithm were about 80.19%, 78.79% and 90.13%, respectively. This study provides important information to the research of snow cover in saline-alkali land.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.