先验知识在被动微波遥感土壤湿度反演中的作用和影响

利用微波遥感的发射率数据反演裸土壤湿度，不可避免需要结合地表面和土壤层的一些先验知识，而先验知识的准确度又将对反演结果的准确度产生一定的影响。文章讨论了地表的高度起伏相关函数形式、土壤温度和土壤质地等三类先验知识，定义了几种不同的土壤湿度反演误差，从而定量地给出三类先验知识的不确定性对土壤湿度反演的影响，指出：基于BSM散射模型和人工神经网络（ANN）的土壤湿度的反演方法是可行的，向ANN输入两种极化的裸土壤表面发射率数据便可反演出裸土壤的湿度，在上述三种先验知识具有一定的不确定性时仍可保证较好土壤湿度反演准确度。     

被动式微波遥感在南极海冰研究中的应用

南极对全球气象、气候变化起着重要的作用，直接影响着大气环流和气候的变化，南极海冰则是影响全球气候的关键因素。然而南极的98％以上的陆地被平均海拔2400多米的冰雪覆盖，有些地区的环境恶劣，高纬度地区人们是不能够到达，人工考察和调查受到限止，因此通过人们亲眼观测的数据相对于整个南极来说只是凤毛麟角。从二十世纪70年代开始由于人造卫星的应用，对冰雪圈进行了连续观测，随之发展的遥感技术和影像光化学处理技术以及影像计算机处理技术，使遥感技术在研究南极海冰年季间分布以及变化特征时成为至关重要的精确数据的获取方式，微波遥感以其自身的优势成为了南极海冰研究的主要方式。     

微波遥感若干前沿技术及新一代空间遥感方法探讨

随着微波遥感技术在国民经济发展、环境和灾害监测、资源勘探、科学研究以及军事中应用的不断深入,这一技术正在经历着前所未有的发展。文章就当前微波遥感发展的若干前沿技术,例如高性能和新型机理、更高频率的开拓、全电磁波综合探测、小型化以及虚拟探测等进行了分析论述,重点介绍了三维成像雷达高度计、干涉式综合孔经微波辐射计以及虚拟探测技术。最后介绍了全球地形测量编队小卫星的设想。     

面向21世纪的中国微波遥感技术发展

综论了中国微波遥感的发展过程、取得的成绩、总体技术现状,并为未来的发展提出了战略性设想及建议;讨论了航天微波遥感若干技术,从新的角度分析了特点,力求搞清当前在微波遥感发展中的一些问题;提出全面正确认识微波遥感和充分开发深层次信息,讨论了各种遥感手段的综合探测问题;提出建立目前用于遥感的全电磁波综合探测系统的问题。     

基于卷积神经网络的光学遥感图像船只检测

目的研究无需进行复杂的图像预处理和人工特征提取,就能提高光学遥感图像的船只检测准确率和实现船只类型精细分类。方法对输入的检测图像,采用选择性搜索的方法产生船只候选区域,用已经标记好的训练样本对卷积神经网络进行监督训练,得到网络参数,然后使用经过监督训练的卷积神经网络提取抽象特征,并对候选区域进行分类,根据船只候选区域的分类概率同时确定船只的位置以及类型。结果与现有的2种检测方法进行对比,实验结果表明卷积神经网络能有效提高船只检测准确率,平均检测准确率达到了93.3%。结论该检测方法无需进行复杂的预处理,能同时对船只进行检测和分类,并能有效提高船只检测准确率。     

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly. To detect vehicles, the presented AEODCNN-VD model employs single shot detector (SSD) with Inception network as a baseline model. In addition, Multiway Feature Pyramid Network (MFPN) is used for handling objects of varying sizes in RSIs. The features from the Inception model are passed into the MFPN for multiway and multiscale feature fusion. Finally, the fused features are passed into bounding box and class prediction networks. For enhancing the detection efficiency of the AEODCNN-VD approach, AEO based hyperparameter optimizer is used, which is stimulated by the energy transfer strategies such as production, consumption, and decomposition in an ecosystem. The performance validation of the presented method on benchmark datasets showed promising performance over recent DL models.     

海水盐度遥感反演精度的理论分析

利用海面微波辐射的理论模型和海水介电常数计算公式,对影响海水盐度遥感反演精度的有关因素进行了分析计算,包括波段的优选、极化方式和入射角的设计、温度和盐度对反演精度的影响。结果表明,降低亮温对温度的敏感性应该作为选择盐度遥感波段时考虑的一个重要条件,双极化、较大入射角的天线工作方式有助于提高盐度遥感的精度,但是在低温或低盐条件下仍难以达到较高的盐度反演精度。     

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Pine wilt disease (PWD) has recently caused substantial pine tree losses in Republic of Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of Republic of Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods, i.e., artificial neural network (ANN) and support vector machine (SVM). Furthermore, compared the accuracy of two types of Global Positioning System (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD. In Anbi, the SVM had an overall accuracy of 94.13%, which is 6.7% higher than the overall accuracy of the ANN, which was 87.43%. We obtained similar results in Wonchang, for which the accuracy of the SVM and ANN was 86.59% and 79.33%, respectively. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected by referring to the benchmarks sited on both locations, while the GPS device 2 is uncorrected device which used the default setting of the GPS only. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 m, while that of the GPS device 2 data was 0.14 m. In conclusion, both classifiers can distinguish between healthy trees and those with PWD based on LC data. LC data can also be used for other types of classification. There were some differences between the hand-held and drone GPS datasets from both areas.     

我国工程地质遥感技术应用特点与若干问题探讨

简要介绍了工程选线、选址勘测中遥感技术的应用概况;阐述了工程地质遥感技术应用的特点,认为工程地质遥感技术应用有别于其他领域;强调了我国工程地质遥感技术应用与其他国家不同,形成了我国自己的应用特点,这些特点是由我国国情决定的;探讨了工程地质遥感技术应用的若干问题,包括遥感图像判释,遥感图像处理、数据综合分析、施工阶段遥感技术应用等。     

遥感技术在上海湿地研究和保护中的贡献

湿地系指不问其为天然或人工、长久或暂时之沼泽地、泥炭地或水域地带，带有或静止或流动、或为淡水、半成水或成水水体者，包括低潮时水深不超过6米的水域。它对人类生存和人类社会的发展起着许多潜在的作用。