浅水多波束测深仪高分辨率波束形成方法研究

常规波束形成技术以其稳健性好、计算量低等特点得到广泛应用,但其空间分辨率受阵元个数限制,不能突破瑞利限。因此,常规波束形成技术应用于高精度浅水多波束测深仪时,存在空间分辨率不足和旁瓣干扰等问题。对比研究了最小方差无失真(MVDR)及多重信号分类(MUSIC)波束形成在浅水多波束测深仪中的应用,给出了浅水多波束测深仪的常规波束形成、MVDR和MUSIC处理方法,并结合能量法和相位法两种底检测测深算法,处理了iBeam8120浅水多波束测深仪外场数据,验证了本文方法的性能。     

利用改进型MODIS火点探测算法实现河北省秸秆焚烧火点识别

为准确地了解河北省秸秆焚烧火点的空间分布,为秸秆焚烧监测的实现、禁烧工作的开展、环境质量改善提供支持.基于MODIS L1B数据、MODIS标准火点产品MOD14、全国秸秆焚烧火点日报数据为基础,采用改进型MODIS火灾探测算法,并通过IDL语言实现,得到秸秆焚烧火点空间分布信息,并进行空间与定量精度分析.研究表明:火点大部分位于河北省南部的一些地区,其中尤以邢台、石家庄、邯郸火点数量最为突出;该算法运算速度快,获取的秸秆焚烧火点数据具有一定检测精度和可靠性,对秸秆焚烧的监测具有一定的实用价值.     

高分辨率遥感影像配准与变化检测一体化处理

为了克服高分辨率遥感影像配准与变化检测作为单独环节处理的局限,该文提出了一种基于变分理论的配准与变化检测一体化处理方法。该方法将配准误差作为一种光谱变化决策因子,变化信息以权值的方式迭代反馈给变分配准模型的解算过程。为了更准确地检测建筑物这个特定目标的真实变化,该文采用多尺度最大形态学轮廓建筑物检测指数的差异作为另外一个决策因子。最后将配准误差反映的变化和建筑物检测指数的差异这两个决策因子在D-S证据理论框架下建立概率模型进行融合处理,进而得到建筑物的变化检测结果。该文选取WorldView-2数据进行实验,实验结果表明,一体化处理思路可以有效地解决单独处理的局限,从根本上解决配准误差对变化检测结果的影响以及由于变化而使配准精度降低的问题,进而提高配准和变化检测的质量。     

An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.     

Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data

It is important to identify and locate glacial lakes for assessing any potential hazard. This study presents a combination of semi-automatic method Double-Window Flexible Pace Search (DFPS) and edge detection technique to identify glacial lakes using Sentinel 2A satellite data. Initially, Normalized Difference Water Index (NDWI) has been used to identify water and non-water areas, while DFPS and Edge detection technique has been used to identify an optimum threshold value to distinguish between water and shadow areas. The optimal threshold from DFPS process is 0.21, while threshold value of gradient magnitude using edge detection process is 0.318. The number of glacial lakes identified using the above algorithm is in close agreement with previously published results on glacial lakes in Gangotri glacier using different techniques. Thus, a combination of DFPS and edge detection process has successfully segregated glacial lakes from other features present in Gangotri glacier.     

Estimation of crowd density from UAVs images based on corner detection procedures and clustering analysis

With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings, crowd monitoring has taken a considerable attentions in many disciplines such as psychology, sociology, engineering, and computer vision. This is due to the fact that, monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents (e.g. sports). One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles (UAVs), because UAVs have the capability to acquiring fast, low costs, high-resolution and real-time images over crowd areas. In addition, geo-referenced images can also be provided through integration of on-board positioning sensors (e.g. GPS/IMU) with vision sensors (digital cameras and laser scanner). In this paper, a new testing procedure based on feature from accelerated segment test (FAST) algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions. The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order. A single pixel which takes the ranking number 9 (for FAST-9) or 12 (for FAST-12) was then compared with the center pixel. Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features. The results show that the proposed algorithms are able to extract crowd features from different UAV images. Overall, the values of Completeness range from 55 to 70 % whereas the range of correctness values was 91 to 94 %.     

基于ETOPO1数据的全球及重点海域水深统计与分析

由于不同的水深探测手段及海深反演方法适用于不同深度的海域,且全球海域水深分布不均,因此在探测及反演海深之前,需对目标海域的水深分布有一定认识。本文采用ETOPO1模型对全球及部分海域进行统计分析,结果表明,全球及大西洋、印度洋、太平洋区域的水深大多处于3 000—10 000 m,其中3 000—5 000 m水深海域占比较大,因此,在这些海域主要采用重力数据结合船测数据进行反演,同时应注意避开海岸带、岛屿较多、海脊存在的区域;对于水深小于1 000 m的海域,可以根据外部环境,采用多波束船测、水深遥感、机载激光测深等方式进行探测。通过分析及统计全球和重点海域的水深可以为海深探测及海底地形反演方法的选取提供参考。     

直线特征辅助的靠岸舰船检测

针对靠岸舰船难以检测的问题,提出了一种直线特征辅助的靠岸舰船检测方法。首先利用高精度的卷积神经网络目标检测算法YOLOv3对影像进行粗检测,获取可能存在舰船目标的区域作为兴趣区域;然后提取影像的直线特征,将直线的方向作为确定舰船方向的辅助信息;最后利用具有一定角度的滑动窗口遍历兴趣区域获取候选目标,并对侯选目标进行二次分类和识别得到最终检测结果。利用不同港口的遥感影像进行实验的结果表明,提出方法能够有效检测港口内多种方向和并列停靠的舰船目标。     

一种适用于粗匹配的遥感影像快速变化发现方法

针对遥感影像变化检测过程中影像间相对配准精度对检测结果影响大,易产生细碎图斑和错检冗余等问题,本文提出了一种基于感知哈希算法适用于粗匹配的遥感影像变化发现方法,以待检测影像的光谱信息和纹理特征信息共同作为检测特征集,以分割后的格网作为检测单元,采用基于感知哈希算法的影像相似度比对方法,计算格网内图像相似度,提取变化位置...     

Detection of the end point temperature of thermal denatured protein in fish and chicken meat through SDS-PAGE electrophoresis

Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) was applied in the detection of the end point temperature (EPT) of thermal denatured protein in fish and meat in this study. It was also used in studying the thermal denatured temperature range of proteins in salmon and chicken meat. The results show that the temperature ranges of denatured proteins were from 65 ℃ to 75 ℃ , and these temperature ranges were influenced by the processing methods. Through SDS-PAGE, the features of repeated he...