The impact of false alarm mitigation on surface landmine detection in MWIR imagery

Surface landmine and minefield detection from airborne imagery is a difficult problem. As part of the minefield detection process, anomaly detection is performed to identify potential landmines in individual airborne images. Post-processing is performed on the initial landmines identified to reduce the number of false alarms, referred to as false alarm mitigation. In this research, a circular harmonics transform image processing approach (the CHT method) and a constant false alarm rate technique (the RX approach) are investigated for surface landmine detection and false alarm mitigation in medium wave infrared (MWIR) image data. The false alarm mitigation approach integrates the CHT and RX methods to identify candidate landmine locations with one technique at a given false alarm rate and applies the other technique to confirm landmine locations and eliminate potential false alarms. Individual detector and false alarm mitigation experimental results are presented for 31 daytime and 43 nighttime MWIR images containing 76 and 142 landmines, respectively. At a 0.9 desired probability of landmine detection, experimental results show that false alarm mitigation reduces the false alarm rate by as much as 84.3% and 13.7% for daytime and nighttime images, respectively, maintaining the probability of detection at 0.85 and 0.90, respectively.     

Detecting and locating landmine fields from vehicle- and air-borne measured IR Images

Air- and vehicle-borne sensor-based technique is a potentially attractive approach for fast detecting landmines and locating landmine fields towards humanitarian demining. For images measured from airborne and vehicle-borne cameras, landmines may be indicated by direct or indirect signs, e.g., spatial difference from their surroundings due to digging or, due to thermal and material signatures. The background in images usually consists of various types of noise and clutter, e.g., thermal noise, sand, gravel road and vegetation, thus making the detection even more difficult. This paper is focused on the following aspects: (1) Finding a robust detector that is suitable for detecting/locating landmine candidates and man-made landmarks by using infrared images measured from vehicle- or air-borne sensors; (2) Interpreting the detector using the 2D isotropic bandpass filter, matched filter, detection theory and thermodynamic-based landmine models; (3) Extending the detector to a multiscale version where landmine detectability is enhanced by automatically selecting a proper scale and localization is improved by inter-scale position tracing. We propose a special type of isotropic feature detector that exploits the characteristic difference between landmines and their surroundings in the spatial-frequency domain under the multiscale framework. Experiments were performed on several infrared images measured from vehicle-borne sensors as well as airborne sensors on a helicopter over the test bed scenarios. The performance of the detector was also evaluated in terms of detectability, localization, and automatic scale selection of the detector. These results and evaluations have shown the effectiveness of the method and its potential in landmine field detection.     

Remote sensing of soil salinity: potentials and constraints

Soil salinity caused by natural or human-induced processes is a major environmental hazard. The global extent of primary salt-affected soils is about 955 M ha, while secondary salinization affects some 77 M ha, with 58% of these in irrigated areas. Nearly 20% of all irrigated land is salt-affected, and this proportion tends to increase in spite of considerable efforts dedicated to land reclamation. This requires careful monitoring of the soil salinity status and variation to curb degradation trends, and secure sustainable land use and management. Multitemporal optical and microwave remote sensing can significantly contribute to detecting temporal changes of salt-related surface features. Airborne geophysics and ground-based electromagnetic induction meters, combined with ground data, have shown potential for mapping depth of salinity occurrence. This paper reviews various sensors (e.g. aerial photographs, satellite- and airborne multispectral sensors, microwave sensors, video imagery, airborne geophysics, hyperspectral sensors, and electromagnetic induction meters) and approaches used for remote identification and mapping of salt-affected areas. Constraints on the use of remote sensing data for mapping salt-affected areas are shown related to the spectral behaviour of salt types, spatial distribution of salts on the terrain surface, temporal changes on salinity, interference of vegetation, and spectral confusions with other terrain surfaces.As raw remote sensing data need substantial transformation for proper feature recognition and mapping, techniques such as spectral unmixing, maximum likelihood classification, fuzzy classification, band ratioing, principal components analysis, and correlation equations are discussed. Lastly, the paper presents modelling of temporal and spatial changes of salinity using combined approaches that incorporate different data fusion and data integration techniques.     

Remote sensing of coastlines: detection,extraction and monitoring

This paper reviews the current status of the use of remote sensing for the detection, extraction and monitoring of coastlines. The review takes the US system as an example. However, the issues at hand can be applied to any other part of the world. Visual interpretation of airborne remote sensing data is still widely and popularly used for coastal delineation. However, a variety of remote sensing data and techniques are available to detect, extract and monitor the coastline. The developed techniques have reached a level of maturity such that they are applied in operational settings.     

Deep feature learning versus shallow feature learning systems for joint use of airborne thermal hyperspectral and visible remote sensing data

Recently, the development of various remote sensing sensors has provided more reliable information and data for identification of different ground classes. Accordingly, multisensory fusion techniques are applied to enhance the process of information extraction from complementary airborne and spaceborne remote sensing data. Most of previous research in the literature has focused on the extraction of shallow features from a specific sensor and on classification of the resulted feature space using decision fusion systems. In recent years, Deep Learning (DL) algorithms have drawn a lot of attention in the machine learning area and have had different remote sensing applications, especially on data fusion. This study presents two different feature-learning strategies for the fusion of hyperspectral thermal infrared (HTIR) and visible remote sensing data. First, a Deep Convolutional Neural Network (DCNN)-Support Vector Machine (SVM) was utilized on the features of two datasets to provide the class labels. To validate the results with other learning strategies, a shallow feature model was used, as well. This model was based on feature fusion and decision fusion that classified and fused the two datasets. A co-registered thermal infrared hyperspectral (HTIR) and Fine Resolution Visible (Vis) RGB imagery was available from Quebec of Canada to examine the effectiveness of the proposed method. Experimental results showed that, except for the computational time, the proposed deep learning model outperformed shallow feature-based strategies in the classification performance that was based on its accuracy.     

航空遥感数据同化算法集成计算与可视化

遥感数据同化技术在动力模型框架内,使用数据同化算法对动力模型输出的定量(物理、化学量)数据与观测数据进行一致性处理与结果误差分析。将多源遥感数据同化到动力模型预测与参数估计中,可帮助改善地表、大气和海洋变化的分析和预测精度。以国家发改委"十二五"建设的国家航空遥感系统项目为依托,针对航空遥感系统10种传感器设计开发数据同化系统。因无法找到适用于该系统的3DVAR和EnKF算法程序,必须自主开发核心算法程序。介绍了研究开发的航空遥感数据同化算法集成计算与可视化系统及其核心算法的关键技术流程。实验结果证实,该系统可以有效地对航空遥感数据进行同化。     

我国微波遥感现状及前景

分析比较了遥感技术中使用的可见光、红外和微波遥感器的特点,介绍了微波遥感理论研究内容和我国的研究现状,叙述了我国机载合成孔径侧视雷达、机载微波辐射计、机载微波散射计、机载海洋雷达高度计的研制和应用状况,指出目前我国机载微波遥感器已基本达到实用水平,并且在生产建设、科学研究和防灾减灾中发挥出作用。列出了国际主要对地观测卫星装载遥感器的情况,从中看出微波遥感器在对地观测中日益增强的作用,建议国家对微波遥感的研究和应用加强统一领导,加强机载实用运行、陆基实验、微波遥感理论研究,以确保早日实现我国自己的星载微波遥感。     

Robust landmine detection from thermal image time series using Hough transform and rotationally invariant features

ABSTRACT

Automated detection of buried anti-personnel landmines using remote sensing techniques is very important for clearing minefields without putting lives in danger. Although thermal infrared imaging is promising, it is far from applicable to the real world in its current state-of-the-art. The most serious problem is that experiments are generally held using sandboxes or levelled and cleared soil, but real fields are, at least partially, covered with plants. In this study, we present an algorithm for landmine detection that is robust enough to detect beyond the clutter caused by partial plant cover. The first part is a hypothesis generator based on circular Hough Transform applied to images that are filtered to enhance circular structures. The second part tests the candidate landmine coordinates using rotationally invariant features, including modified Histogram of Oriented Gaussians (HOG), over multiple images taken at different times after Wiener filtering to maximize signal-to-clutter ratio. The performances of various features and classifiers are compared. The overall performance of the algorithm is demonstrated on a dataset of real-world landmine images contaminated by simulated plants. Satisfactory results are obtained up to 40% equivalent plant coverage where more than 65% of the pixels are fully or partially covered by plants.     

基于自编码神经网络的航空物探遥感数据分类方法研究

航空物探遥感数据的采集过程中受到电磁波辐射等外界因素的影响,导致航空物探遥感数据分类准确率较低,为此提出基于自编码神经网络的航空物探遥感数据分类方法;根据航空物探对象的基本特征,设置遥感数据的分类标准;通过辐射校正、几何纠正、噪声消除等步骤,完成航空物探遥感数据的预处理;构建自编码神经网络,利用自编码神经网络算法,从光谱、形状、纹理等方面提取遥感数据特征,通过特征匹配确定航空物探遥感数据的所属类型;通过分类性能测试实验得出结论：所提方法的全局遥感数据分类成功率和错误率的平均值分别为99.8%和0.6%,局部遥感数据分类的成功率和错误率的平均值分别为99.8%和0.3%,即所提方法在分类性能方面具有明显优势。     

赤潮遥感进展与算法研究

回顾了国内外赤潮遥感研究概况,介绍赤潮遥感的生物物理化学依据,并总结利用卫星光学遥感数据反演赤潮信息的算法研究成果。     

Les systèmes légers de télédétection aéroportée pour les sciences de la Terre

Satellite remote sensing, a powerful monitoring tool, often lacks the high spatial resolution and operational flexibility needed for research in Earth Sciences. In order to highlight the concept of linking ground data and remote sensing imagery, a review of tremendous recent advances in low-altitude remote sensing, as well as in supporting systems such as light airborne platforms is presented. Airborne digital photography and videography provide near real-time data at the local and regional scales. The integration of portable multi-spectral digital sensors, navigation systems and small slow-flying aircraft creates a modern do-it-yourself data acquisition tool for everyday field work and ground truthing of satellite imagery. This approach closes the existing gap between the advanced spaceborne remote sensing methods and the precise but inefficient ground studies. It is particularly advantageous for geomorphology and Earth Sciences.     

航天遥感图象压缩技术的最新发展

介绍了当今图象压缩技术的发展现状，以及航天遥感数据压缩技术的应用现状，探讨了上前各种图象压缩技术的特点，并说明了各种技术应用航天遥感的可能性，对将来我国航天遥感星载无损压缩，有损压缩以及地面数据压缩提出了建议。     

Generating cloudmasks in spatial high-resolution observations of clouds using texture and radiance information

The detection of clouds in measurements taken by airborne and spaceborne remote sensing sensors in the visible and near-infrared is often difficult due to the high albedo of underlying surfaces such as snow- and ice-covered surfaces as well as sunglint regions of water surfaces. The authors show that the measured intensity of the reflected solar radiation together with texture information is effective in detecting clouds over water surfaces which are affected by sunglint. An automated cloud-masking technique for images measured by a compact airborne spectrographic imager ( casi ) during the ACE-2 CLOUDYCOLUMN experiment has been developed based on supervised learning of an artificial neural network. The neural network has been trained on radiances, texture features, and gradient-filtered radiances. The radiances were measured at a single wavelength but with high spatial resolution so that characteristic spatial features within an image can be used to discriminate clouds from sunglint, cloud shadow and ocean surface. The accuracy of the cloudmask-generating algorithm was investigated on the basis of the testing set for the neural network. Maximum errors of 3.4% and 1% occur for detecting cloudy and cloud-free pixels, respectively. The performance of the network was compared with a second network trained on radiances alone. The second network is up to 44% less efficient for cloud detection which demonstrates the improvement arising from the use of texture information together with spatial high-resolution observations.     

航空数字相机的发展与应用

90年代迅速发展的航空数字相机已向传统的航空胶片摄影发出了挑战,它建立在当代多项 高技术成果基础上并具有一系列优点,在各种遥感应用领域已显现了巨大的应用潜力。重点介绍了 其国际发展概况,并就空间分辨率、作业效率、采集存贮技术、图像质量、平台运动影响、光谱响应和 立体成像能力等问题进行了分析。预计它将是21世纪航空遥感最具发展潜力的技术手段之一。     

A comparison of decision-level sensor-fusion methods for anti-personnel landmine detection

We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The system has three sensor types: a metal detector (MD), an infrared camera (IR), and a ground penetrating radar (GPR). The output of the sensors is processed to produce confidence levels on a grid covering the test-bed. A confidence level expresses a confidence or belief in a landmine detection on a certain position. The grid with confidence levels is the input for the decision-level sensor-fusion and provides a co-registration of the sensors. The applied fusion methods are naive Bayes' approaches, Dempster–Shafer theory, fuzzy probabilities, a rule-based method, and voting techniques. To compare fusion methods and to analyse the capacity of a method to separate landmines from the background on the basis of the output of different sensors, we provide an analysis of the different methods by viewing them as discriminant functions in the sensor confidence space. The results of experiments on real sensor data are evaluated with the leave-one-out method.     

航空遥感技术系统某些灾害监测中的应用

以地震，洪水和森林火灾等突发性自然灾害的遥感监测为研究目标，简要论述了航空遥感技术系统的一些应用示例。航空遥感技术系统具有机动灵活和快速反应能力，在灾害的实时动态监测中的发挥重要作用，根据不灾种选择相应的传感器配套系统，快速地到达灾区获取灾情图像；通过机－地传输系统将图像快速传输到地面接收站，地面快速图像处理系统实施快视，图像预处理和人－机交互解译，确定受灾程度及空间分布，供当地政论在实施救援活动     

航空遥感技术系统在某些灾害监测中的应用

以地震、洪水和森林火灾等突发性自然灾害的遥感监测为研究目标，简要论述了航空遥感技术系统的一些应用示例。航空遥感技术系统具有机动灵活和快速反应能力，在灾害的实时动态监测中发挥重要作用。根据不同灾种选择相应的传感器及配套系统，快速地到达灾区获取灾情图像；通过机－地传输系统将图像快速传输到地面接收站，地面快速图像处理系统实施快视、图像预处理和人－机交互解译，确定受灾程度及空间分布，供当地政府在实施救援活动中采用；远程通讯系统将编码压缩后的灾情图像传输到北京指挥中心便于采取更有利的救援措施。     

国际遥感的现状和发展趋势

自1972年美国第一颗陆地卫星发射以来,遥感在世界范围内得到了迅速的发展和广泛的应用,引起了各界人士和许多国家的关注和重视。但是,从目前遥感,特别是航天遥感的技术水平和应用状况来看,尚不能满足有效地解决各应用领域中不断地提出来的各种实际问题的需要。     

Reflectance assessment of canopy CO2 uptake

The potential of using visible and near-infrared reflectance for remote assessment of plant photosynthetic processes has been developed recently at the leaf and proximal canopy levels (Penñuelas and Filella 1998). This Letter presents a further step in such development by using visible reflectance to monitor net CO₂ uptake of a whole 4 ha crop field, thus opening the possibility for applications at larger areas with remote sensing from airborne sensors.