高光谱遥感影像波段选择算法评价方法研究

波段选择在高光谱影像数据降维处理中尤为重要,其算法较多,但针对波段选择算法评价方法的研究却很少。该文提出一种波段选择算法的评价方法。引入信息量、类别可分性和相关性3个指标,对所选波段子集进行度量,通过比较不同波段选择算法所选波段子集的度量结果,实现对波段选择算法的评价。在试验中,将该评价方法应用于4种常用的波段选择算法,分析了评价结果并验证了该评价方法的可行性。     

基于重庆主城区居民感知的城市意象元素分析评价

城市意象是诠释一个城市环境的重要指标,对其分析研究,有助于解读及评估城市景观的优劣、城市生活环境品质的高低,也有助于把握城市空间结构。本文首先在回顾国内外相关研究进展的基础上,根据重庆城市特色,将Lynch K的5类城市意象要素进行细分,并转换为地点场所意象元素;其次采用问卷调查法,对城市意象因子和意象元素分别进行评价,并绘制城市意象元素分布图,以了解居民对城市意象元素的印象与品质之间的关系,从而为重庆城市规划建设提供有益的参考。     

基于小波变换的射频干扰消除方法的研究

在射电天文观测中,射频干扰(Radio Frequency Interference, RFI)会以多种形式混入望远镜接收系统,给观测带来误判或者降低观测信噪比.近年来国内国际射电天文快速发展,国内国际大型射电望远镜和阵列先后建设,观测灵敏度大为提高,射频干扰的影响尤为突出.随着科技发展和人类活动的加剧,射频干扰日益严重且不可逆转.提出利用2维离散小波变换的方法分析射电天文观测的数据,对望远镜系统输出的时间频率序列进行小波变换,根据小波系数分离出原始信号中各分量,每个分量统计得到相应的阈值,将各分量与阈值相比较识别干扰成分并标记去除.利用该方法对实际观测数据进行了处理,结果表明该方法能够很好地标记并消减干扰信号,且提高了观测的信噪比.     

基于旋翼无人机观测的雾天和霾天VOCs垂直分布特征研究

为研究雾和霾天气下VOCs时空变化特征,于2020年11月19日—2021年1月15日在江苏省东海国家气象观测站进行为期58 d的外场观测试验。利用自主研发的多旋翼无人机捕获2次辐射雾和2次霾天气过程,获得气温、气压、相对湿度、风向、风速、VOCs、O₃等7种要素100多条垂直廓线。结果表明：时间上,霾过程夜间VOCs体积浓度(0.225～0.253 ppm(parts per million, 1 ppm=10^-6))明显高于白天(0.191～0.205 ppm),雾形成前体积浓度(0.121～0.239 ppm)显著高于雾过程(0.056～0.209 ppm)。雾过程中VOCs体积浓度与雾强度变化相反,雾层高度与VOCs体积浓度剧烈变化高度一致,雾层(<200 m)中VOCs体积浓度(0.172～0.178 ppm)明显减小,显著低于雾形成前(0.195～0.240 ppm),雾层以上浓度变化大,雾结束后1 h内保持雾过程中分布特点。雾对逆温层中的水溶性污染物有清除作用,VOCs体积浓度和O₃质量浓度均下降。     

基于多格网算法的卫星图像恢复

将求解二次椭圆偏微分方程问题的多格网算法应用到卫星影像的恢复中。其基本思想是在将由总变分求极小生成的偏微分方程离散成为差分方程的过程中,采用不同大小的格网。在取得较好恢复结果的同时,提高了传统图像恢复算法的运算效率,降低了恢复运算所需要的时间。     

无人机载线阵摆扫CCD影像几何校正

针对无人机载线阵CCD传感器摆扫成像的技术特点,利用GPS/INS组合导航系统提供的导航遥测数据,对无人机载的线阵摆扫CCD影像建立了严格的成像模型,并设计了详细的几何校正流程。实验结果证实采用文中的成像模型和几何校正流程,可获得正确、高精度地理编码的线阵摆扫CCD正射影像。     

东南极格罗夫山GPS控制网的布设与数据处理

为了给中国南极野外考察队提供DEM卫星影像图,在2002/2003年南极度夏期间,中国第19次南极考察队的大地测量工作者,在东南极Grove山地区,进行了地面控制测量。在覆盖面积达8000km~2的2000多米高的内陆冰盖高原区,在直升机和雪地车的支持下,利用Trim- ble4000ssi GPS接收机,与中山站GPS跟踪站联测,测量了7个GPS控制点,并埋设了永久性测量标志。数据处理采用GAMIT/GLOBK软件解算,获得了较好的定位精度,能够满足该地区制图所需。     

An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.     

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.