消费级多旋翼无人机1∶500大比例尺测图的应用

消费级多旋翼无人机技术不断成熟,使其用于摄影测量成为现实。利用消费级四旋翼无人机搭载普通相机,开展1∶500大比例尺测图任务,针对消费级无人机和非量测相机的影像采集特点,制定项目实施方案并开展数据生产,实验结果表明：利用消费级无人机进行大比例尺测图的方案可行,测图精度满足规范要求;利用采集影像生成的测区实景三维模型辅助内业成图,能够大幅减少外业调绘工作量。研究表明该方案提高了大比例尺测图的工作效率,减少了项目的总体成本,有很好的经济效益和应用前景。     

消费级多旋翼无人机1∶500大比例尺测图的应用

消费级多旋翼无人机技术不断成熟,使其用于摄影测量成为现实。利用消费级四旋翼无人机搭载普通相机,开展1∶500大比例尺测图任务,针对消费级无人机和非量测相机的影像采集特点,制定项目实施方案并开展数据生产,实验结果表明:利用消费级无人机进行大比例尺测图的方案可行,测图精度满足规范要求;利用采集影像生成的测区实景三维模型辅助内业成图,能够大幅减少外业调绘工作量。研究表明该方案提高了大比例尺测图的工作效率,减少了项目的总体成本,有很好的经济效益和应用前景。     

消费级多旋翼无人机1∶500大比例尺测图的应用

消费级多旋翼无人机技术不断成熟，使其用于摄影测量成为现实。利用消费级四旋翼无人机搭载普通相机，开展1∶500大比例尺测图任务，针对消费级无人机和非量测相机的影像采集特点，制定项目实施方案并开展数据生产，实验结果表明：利用消费级无人机进行大比例尺测图的方案可行，测图精度满足规范要求；利用采集影像生成的测区实景三维模型辅助内业成图，能够大幅减少外业调绘工作量。研究表明该方案提高了大比例尺测图的工作效率，减少了项目的总体成本，有很好的经济效益和应用前景。     

无人机影像目标检测技术研究进展

无人机影像目标检测技术是无人机影像智能化分析中的关键技术，开展无人机影像目标检测技术的研究有着广阔的应用前景和价值。介绍了无人机影像中目标检测技术的发展历程，简述了无人机影像目标检测技术在森林防火、农业信息采集、电力线路检测、路桥损害检测以及军事侦察等5种特定领域的应用情况，总结归纳了目标检测性能评价时常用的数据集和标准，并对未来无人机影像目标检测技术的发展态势进行了展望。     

基于第二次土地调查的大区域SPOT5 影像工程化处理研究

国土资源部即将在全国开展的新一轮土地调查, 并建立土地变更调查制度和管理系统。相较于上世纪80 年代的第一次土地详查, 新一轮土地调查的特点在于技术新、手段齐, 查得全、查得清。本文针对第二次土地调查, 以工作中普遍采用的大区域多景连片SPOT 5 卫星影像为例, 讨论了SPOT 5 卫星影像的正射纠正方法, 详细介绍了影像纠正前根据参考图选点误差、数字高程模型的误差精度和传感器内部结合误差三方面对影像纠正应该达到的精度理论值进行评估, 以及纠正后对影像几何精度的精度评定的方法, 阐述了SPOT 5 卫星影像处理的工程化管理方法。     

无人驾驶飞机精确定位方法研究

GPS技术已广泛应用于无人驾驶飞机的空间定位和导航,但在战时、困难地区的应用会受到限制。文章提出了一种利用多点约束的影像匹配技术实现无人机精确定位的方法,该方法基于多点约束的思想提高了影像匹配的可靠性,同时利用匹配获取的多个同名点解决了由于无人机空中姿态变化引起的单点无法精确定位问题。该方法首先对无人机获取的实时影像和基准影像进行小波变换处理,以便从实时影像上获得足够的明显特征点,然后利用影像匹配和匹配点之间的位置约束关系获取基准影像上的同名点位,使用这些同名点,可以得到精确的无人机空间位置。     

无人机遥感影像获取及后续处理探讨

作为卫星遥感和航空遥感的有益补充,无人机航空遥感系统获取遥感影像具有多种特性。通过4次无人机航拍试验,根据所获取的遥感影像和飞行辅助数据,对航拍数据进行拼接。从航拍的多个方面对飞行试验以及实验成果进行了质量评价。并提出了无人机应用于航拍时存在的问题及一些改进方法。     

利用影像匹配和摄影测量实现无人机精确导航

GPS技术已广泛应用于无人机的空间定位和导航,但在战时、困难地区的应用会受到限制.本文提出了一种利用影像匹配和摄影测量原理实现无人机精确定位和导航的方法,首先对无人机获取的实时影像和基准影像进行小波变换处理,以获得足够的初选同名点位,然后利用最小二乘影像匹配获取精确的同名点坐标,使用这些同名点,经摄影测量处理得到了精确的无人机空间位置.     

小型四旋翼无人机序列影像三维重建实验

传统航空影像三维重建受到时效性、成本等限制,而无人机三维重建技术是解决中小范围三维地形问题的有效手段。基于小型四旋翼无人机航拍序列影像开展了三维重建实验。首先分析了小型无人机航拍三维重建流程的特点,随后重点讨论了无人机三维重建中涉及的关键问题,包括无人机飞行姿态控制、航速、航高、重叠度和拍照模式选择等,最后对比了不同软件的重建结果。实验结果表明,通过优化控制无人机飞行姿态、航高、航速、重叠度等因素,选择合理的序列影像获取模式,在不同的软件中均可完成精度较高的精细地物三维重建。     

面向滑坡的旋翼机正视/侧视航摄处理与分析

针对滑坡地形正视航空摄影时单张影像、不同航带影像分辨率不一致的问题,提出一种基于滑坡角度开展正视/侧视航空摄影的数据获取与处理方法,并且在四川滑坡地质灾害点开展了对比分析实验。实验结果表明,利用旋翼无人机对滑坡灾害体进行按滑坡角度侧视航空摄影以及投影到滑坡面进行数据处理,能够有效解决单张影像分辨率不一致以及不同航带影像分辨率不一致的问题,利用该方法处理滑坡地形有利于滑坡体解译与分析。     

基于3S的土地利用数据库更新技术研究

以湖南省株洲市天元区土地利用数据库为研究对象,探索利用3S技术对土地利用数据库进行更新的技术和方法.利用静态GPS实测地面控制点,ERMAPPER为平台对QuickBird卫星数据进行几何精校正,校正后图像的点位误差控制在1个像元左右,以此作为标准校正SPOT-5、ETM＋和TM图像.将校正后SPOT-5图像导入MAPGIS与同期的土地利用现状图匹配,对发生变化的地块逐个进行标记,共标识图斑189个.在试验区内建立高精度GPS基站和移动站一套,利用移动站对变化图斑进行野外实测,数据经格式转换后导入MAPGIS 内,对土地利用现状的图形库和属性库分别进行更新.结果表明：采用3S技术可以快速、准确、高效地更新土地利用数据库,实现土地利用现状短周期预测预报的目的.     

A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis

Rapid urban growth in developing countries is causing a great number of urban planning problems. To control and analyse this growth, new and better methods for urban land use mapping are needed. This article proposes a new method for urban land-use mapping, which integrates spatial metrics and texture analysis in an object-based image analysis classification. A high-resolution satellite image was used to generate spatial and texture metrics from the machine learning algorithm of Random Forests land-cover classification. The most meaningful spatial indices were selected by visual inspection and then combined with the image and texture values to generate the classification. The proposed method for land-use mapping was tested using a 10-fold cross-validation scheme, achieving an overall accuracy of 92.3% and a kappa coefficient of 0.896. These steps produced an accurate model of urban land use, without the use of any census or ancillary data, and suggest that the combined use of spatial metrics and texture is promising for urban land-use mapping in developing countries. The maps produced can provide the land-use data needed by urban planners for effective planning in developing countries.     

A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification

Previous works about spatial information incorporation into a traditional bag-of-visual-words (BOVW) model mainly consider the spatial arrangement of an image, ignoring the rich textural information in land-use remote-sensing images. Hence, this article presents a 2-D wavelet decomposition (WD)-based BOVW model for land-use scene classification, since the 2-D wavelet decomposition method does well not only in textural feature extraction, but also in the multi-resolution representation of an image, which is favourable for the use of both spatial arrangement and textural information in land-use images. The proposed method exploits the textural structures of an image with colour information transformed into greyscale. Moreover, it works first by decomposing the greyscale image into different sub-images using 2-D discrete wavelet transform (DWT) and then by extracting local features of the greyscale image and all the decomposed images with dense regions in which a given image is evenly sampled by a regular grid with a specified grid space. After that, the method generates the corresponding visual vocabularies and computes histograms of visual word occurrences of local features found in each former image. Specifically, the soft-assignment or multi-assignment (MA) technique is employed, accounting for the impact of clustering on visual vocabulary creation that two similar image patches may be clustered into different clusters when increasing the size of visual vocabulary. The proposed method is evaluated on a ground truth image dataset of 21 land-use classes manually extracted from high-resolution remote-sensing images. Experimental results demonstrate that the proposed method significantly outperforms previous methods, such as the traditional BOVW model, the spatial pyramid representation-based BOVW method, the multi-resolution representation-based BOVW method, and so on, and even exceeds the best result obtained from the creator of the land-use dataset. Therefore, the proposed approach is very suitable for land-use scene classification tasks.     

Land-use scene classification based on a CNN using a constrained extreme learning machine

ABSTRACT

Classifying land-use scenes from high-resolution remote-sensing imagery with high quality and accuracy is of paramount interest for science and land management applications. In this article, we proposed a new model for land-use scene classification by integrating the recent success of convolutional neural network (CNN) and constrained extreme learning machine (CELM). In the model, the fully connected layers of a pretrained CNN have been removed. Then, CNN works as a deep and robust convolutional feature extractor. After normalization, deep convolutional features are fed to the CELM classifier. To analyse the performance, the proposed method has been evaluated on two challenging high-resolution data sets: (1) the aerial image data set consisting of 30 different aerial scene categories with sub-metre resolution and (2) a Sydney data set that is a large high spatial resolution satellite image. Experimental results show that the CNN-CELM model improves the generalization ability and reduces the training time compared to state-of-the-art methods.     

基于3S技术的土地利用/土地覆盖变化野外采样框架设计 ——以东北地区黑龙江省为例

土地利用/土地覆盖变化是全球环境变化的重要组成部分,随着3S技术的不断成熟和发展,运用RS、GPS和GIS技术进行土地利用/土地覆盖变化研究已成为一种越来越成熟的方式和手段。从空间抽样模型理论出发,以我国黑龙江省为例,运用RS、GPS和GIS技术,通过对黑龙江省道路网、土地利用区划、土地利用/土地覆盖类型、土地利用/土地覆盖1 km×1 km格网数据等空间信息分布的综合考虑、分析,设计了土地利用/土地覆盖变化的综合野外采样框架。框架主要包括采样区的布设、采样路线和采样点的选择等。由于以多层空间信息为采样依据,经实践检验,该采样框架具有经济实用等优点。     

Inferring urban land use using the optimised spatial reclassification kernel

In the 1990s, promising results in land-use classification were obtained by kernel-based contextual classification algorithms. Soon, however, it was recognised that kernel-based reclassifiers have important shortcomings and research instead focused on object-based image analysis. This study proposes a solution to two of the most important drawbacks of kernel-based reclassifiers: (1) the use of kernels tends to smooth boundaries between discrete land-use/land-cover parcels, and (2) it is difficult to determine a priori the optimum kernel size of the classifier. The Spatial Reclassification Kernel (SPARK) algorithm has been adapted in order to automatically optimise the kernel size depending on the spatial variation found in the neighbourhood of a pixel to be classified, resulting in the Optimised SPARK (OSPARK) algorithm. The performance of SPARK and OSPARK for land-use classification has been evaluated for the Dublin urban area (Ireland), using a Landsat TM image. The MOLAND land-use map of 1990 was used as a reference. Results show that the use of optimal kernel sizes instead of fixed kernel sizes reduces the omission and commission errors for most land-use classes.     

基于ASTER影像的高山峡谷区主要地类自动提取方法研究

针对高山峡谷地区遥感图像变异较大的特点,通过分析高山峡谷区的光谱结构特征,利用比值植被指数RVI、归一化植被指数NDVI及数字高程模型DEM等数据资料,结合综合阈值法,设定合适的阈值,得到分类单体图的二值化图像,并最终在ArcGIS9.0平台上进行单体图像的合成与输出。与传统最大似然法的分类结果相比,该方法在一定程度上克服了“同物异谱”、“异谱同物”等现象对分类结果带来的影响,提高了高山峡谷区地表覆盖分类的精度,得到了试验区较为可靠的遥感分类图像。     

Land-use mapping and change detection in a coal mining area - a case study in the Jharia coalfield,India

In an area like the Jharia coalfield (JCF), where extensive and rapid underground and opencast mining is going on continuously, land-use studies are of paramount importance. This paper discusses the remote sensing-GIS techniques used for identification of various land-use classes on satellite imagery and enhanced products and identification of time-sequential changes in land-use patterns. The various land-use classes, recognised from satellite image data and field surveys, are dense vegetation, sparse vegetation, fire, opencast mining (coal), overburden dump, subsidence and barren wasteland, settlement, transport network, river and water pond. A number of image processing operations have been carried out on remote sensing data for enhancing land-use patterns. It has been found that Landsat TM false colour composites (FCC) of bands 4, 3 and 2; FCC of bands 7, 5 and 3; FCC of bands 5, 4 and 2 and ratio images provide very useful information for land-use mapping. The normalised difference vegetation index (NDVI) images have been used for vegetation studies. Image characters of various land-use classes on black-and-white and enhanced colour products have been tabulated. Land-use maps of selected windows have been prepared and examples given. Time-sequential surface changes that have occurred in the JCF since 1975 and particularly between November 1990 to November 1994 have been investigated. For change detection analysis, data manipulation in several steps involving preprocessing, processing and colour display have been carried out. Land-use changes have been detected by (a) image differencing, (b) image ratioing, and (c) differencing of NDVI images. It is inferred from the remote sensing images that extensive mining, establishment of communication networks, expansion of settlements, decrease in the vegetation cover etc., have remodelled the face of the JCF.     

An integrated spatio-temporal classification method for urban fringe change detection analysis

The urban fringe is the transition zone between urban land use and rural land use. It represents the most active part of the urban expansion process. Change detection using multi-temporal imagery is proven to be an efficient way to monitor land-use/land-cover change caused by urban expansion. In this study, we propose a new multi-temporal classification method for change detection in the urban fringe area. The proposed method extracts and integrates spatio-temporal contextual information into multi-temporal image classification. The spatial information is extracted by object-oriented image segmentation. The temporal information is modelled with temporal trajectory analysis with a two-step calibration. A probabilistic schema that employs a global membership function is then used to integrate the spectral, spatial and temporal information. A trajectory accuracy measurement is proposed to assist the comparison on the performances of the integrated spatio-temporal method and classical pixel- and ‘snapshot’-based classification methods. The experiment shows that the proposed method can significantly improve the accuracies of both single scene classification and temporal trajectory analysis.     

Comparative flood area analysis of C-band VH,VV, and L-band HH polarizations SAR data

The role of synthetic aperture radar (SAR)-image-based flood area mapping is proved beyond the doubts. It is also well known that different wavelength, polarization SAR reacts in varying ways over the same land-use/land-cover region. In line to this, this article mainly brings out the significance of comparing and analysing different wavelength, polarization SAR data of the same inundated region against the land-use classes of the study area. The C-band ENVISAT advanced synthetic aperture radar data of vertically transmitted horizontally received (VH), vertically transmitted vertically received (VV) polarizations data, and L-band ALOS-1 PALSAR data of horizontally transmitted horizontally received (HH) polarization data has been obtained as both these satellites captured the same flood event of Andhra Pradesh state of India. Initially, the SAR images are classified with the help of digital elevation model of the disaster region which supports in mapping the fully submerged, partially submerged and non-flooded pixels of disaster region. The fully submerged regions includes the natural waterbodies, adjacent flood plain regions which are completely submerged, as well as not accessible, whereas the partially submerged regions are spatially discontinuous and scattered regions which are inundated due to recent disaster but accessible. In this study, much emphasis has been given in comparing and analysing the fully submerged, partially submerged, and non-flooded regions of classified SAR images against each land use of the disaster region by which the response of individual land-use units of the disaster region at different wavelength, polarization has been brought out. From this comparative assessment, it has been observed that the areal extent of fully submerged regions is considerably more in L-band HH image than in the C-band polarization images. It is also been noticed that C-band VH polarization image is able to map and quantify considerable part of the land-use classes as partially submerged regions than the L-band HH polarization image. In addition to this, the proposed technique is able to rectify in classifying mangrove regions as non-flooded regions due to the land-use/land-cover-based approach.