The Z/I Imaging Digital Camera System

Market needs for airborne and spaceborne imagery used in photogrammetry and GIS applications are changing. Fundamental changes in sensors, platforms and applications are currently taking place. Most recently, new high resolution spaceborne sensors have become available. Besides classical photogrammetry, new thematic applications will drive the future image market. Savings in cost and time, together with the need for higher and reproducible radiometric resolution or spectral information will push forward the change from analogue to digital imagery. High resolution satellites will compete with airborne film-based photography and digital camera systems.
With the availability of a digital airborne camera, it is possible to completely close the digital chain from image acquisition to exploitation and data distribution. The key decision regarding the camera design in this case is whether a linear or area array sensor should be used. In view of the high geometric accuracy requirements in photogrammetry, Z/I Imaging has focused development on a digital camera based on an area sensor. An essential aspect of this decision was not only the aerial camera system, but also the entire photogrammetric process to the finished photographic or mapping product. If this point of view is adopted, it becomes clear that the development of a digital camera involves more than simply exchanging film for silicon. Aspects such as data transfer rates, in-flight data processing and storage, image archiving, georeferencing, colour fusion, calibration and preprocessing have the same influence on the economic assessment of a digital camera system. This paper describes current development activities and application aspects of a digital modular airborne camera system.     

地理信息系统集成平台框架结构研究

提出了基于客户／服务器结构的地理信息系统集成平台总体结构,探讨了基于元数据的地理信息系统数据集成平台以建立物理上分布而逻辑上集中的分布式地理信息系统数据库,提出了应用符合３ＮＦ范式的关系数据库进行模型管理的模式,在此基础上探讨了地理信息系统可视化建模工具。     

Object-based urban land cover classification using rule inheritance over very high-resolution multisensor and multitemporal data

Very high spatial and temporal resolution remote sensing data facilitate mapping highly complex and diverse urban environments. This study analyzed and demonstrated the usefulness of combined high-resolution aerial digital images and elevation data, and its processing using object-based image analysis for mapping urban land covers and quantifying buildings. It is observed that mapping heterogeneous features across large urban areas is time consuming and challenging. This study presents and demonstrates an approach for formulating an optimal land cover classification rule set over small representative training urban area image, and its subsequent transfer to the multisensor, multitemporal images. The classification results over the training area showed an overall accuracy of 96%, and the application of rule set to different sensor images of other test areas resulted in reduced accuracies of 91% for the same sensor, 90% and 86% for the different sensors temporal data. The comparison of reference and classified buildings showed ±4% detection errors. Classification through a transferred rule set reduced the classification accuracy by about 5%–10%. However, the trade-off for this accuracy drop was about a 75% reduction in processing time for performing classification in the training area. The factors influencing the classification accuracies were mainly the shadow and temporal changes in the class characteristics.     

结合nDSM的高分辨率遥感影像深度学习分类方法

针对高分辨率遥感影像因其地物类内差异大、光谱信息相对欠缺导致现有影像分类方法存在错分现象较多、地物边界残缺不完整等问题,本文提出了一种归一化数字表面模型（nDSM）约束的高分辨率遥感影像深度学习分类方法。首先,将nDSM数据作为附加波段叠加在遥感影像上并获取训练样本;然后,利用优化的U-Net网络进行模型训练得到最优模型;最后,利用最优模型对附加了nDSM波段的遥感影像进行地物分类。试验结果表明,本文方法引入nDSM数据用于U-Net模型训练和分类,可有效提高影像分类精度,得到更加真实可靠的分类结果。     

Monitoring cross-border trails using airborne digital multispectral imagery and interactive image analysis techniques

The objective of this study was to evaluate image-based procedures for monitoring cross-border foot trails in the US – Mexico border zone in eastern San Diego County using airborne remote sensing techniques. Specifically, digital multi-spectral and multi-temporal imagery from an airborne digital multi-spectral imaging system, digital image processing, and visual image analysis techniques were explored in the context of detecting and delineating new trail features and updating trail GIS layers. Three trail updating approaches: map-to-image (M-I) overlay, map and image-to-image (M/I-I) differencing, map and image-to-image (M/I-I) swiping and two types of spectral transform, PCA and NDVI, were tested and compared. The M-I overlay was found to be the most reliable trail updating approach. The optimal image enhancement method for the M-I overlay approach varied with vegetation structure. PCA imagery yielded better results than NDVI imagery in a highly disturbed area and NDVI imagery performed better in a densely vegetated area. The M/I-I swiping approach was useful for distinguishing misregistered extant trails from new trail features.     

Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data

Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m² (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas.     

Lidar遥感基本原理及其发展

介绍了航空激光扫描(Airborne laser scanning)或者Lidar遥感信息获取系统的基本原理、系统的组成、数据获取的方法及其步骤;对近数十年来应用激光扫描遥感信息获取地形表面模型方面取得的主要成果、应用现状做了简要回顾和评述;结合GIS和影像融合方法对Lidar遥感技术未来发展趋势进行了展望。     

High Resolution DEM Generation in High‐Alpine Terrain Using Airborne Remote Sensing Techniques

Up‐to‐date and accurate digital elevation models (DEMs) are essential for many applications such as numerical modeling of mass movements or mapping of terrain changes. Today the Federal Department of Topography, swisstopo, provides Digital Terrain Models (DTMs) and Digital Surface Models (DSMs) derived from airborne LiDAR data with a high spatial resolution of 2 m covering the entire area of Switzerland below an elevation of 2000 m a.s.l.. However, above an elevation of 2000 m a.s.l., which is typical for high‐alpine terrain, the best product available is the a DTM with a spatial resolution of 25 m. This spatial resolution is insufficient for many applications in complex terrain. In this study, we investigate the quality of DSMs derived from opto‐electronic scanner data (ADS80; acquired in autumn 2010) using photogrammetric image correlation techniques based on the multispectral nadir and backward looking sensor data. As reference, we take a high precision airborne LiDAR data set with a spatial resolution of ca. 0.5 m, acquired in late summer 2010, covering the Grabengufer/Dorfbach catchment near Randa, VS. We find the deviations between the two datasets are surprisingly low. In terrain with inclination angles of less than 30° the RMSE is below 0.5 m. In extremely steep terrain of more than 50° the RMSE goes up to 2 m and outliers increase significantly. We also find dependencies of the deviations on illumination conditions and ground cover classes. Finally we discuss advantages and disadvantages of the different data acquisition methods.     

Object-oriented and pixel-based classification approaches to classify tropical successional stages using airborne high–spatial resolution images

Airborne high–spatial resolution images were evaluated for mapping purposes in a complex Atlantic rainforest environment in southern Brazil. Two study sites, covered predominantly by secondary evergreen rainforest, were surveyed by airborne multispectral high-resolution imagery. These aerophotogrammetric images were acquired at four spectral bands (visible to near-infrared) with spatial resolution of 0.39 m. We evaluated different data input scenarios to suit the object-oriented classification approach. In addition to the four spectral bands, auxiliary products such as band ratios and digital elevation models were considered. Comparisons with traditional pixel-based classifiers were also performed. The results showed that the object-based classification approach yielded a better overall accuracy, ranging from 89% to 91%, than the pixel-based classifications, which ranged from 62% to 63%. The individual classification accuracy of forest-related classes, such as young successional forest stages, benefits the object-based approach. These classes have been reported in the literature as the most difficult to map in tropical environments. The results confirm the potential of object-based classification for mapping procedures and discrimination of successional forest stages and other related land use and land cover classes in complex Atlantic rainforest environments. The methodology is suggested for further SAAPI acquisitions in order to monitor such endangered environment as well as to support National Land and Environmental Management Protocols.     

Towards 3D map generation from digital aerial images

This paper describes the fusion of information extracted from multispectral digital aerial images for highly automatic 3D map generation. The proposed approach integrates spectral classification and 3D reconstruction techniques. The multispectral digital aerial images consist of a high resolution panchromatic channel as well as lower resolution RGB and near infrared (NIR) channels and form the basis for information extraction.Our land use classification is a 2-step approach that uses RGB and NIR images for an initial classification and the panchromatic images as well as a digital surface model (DSM) for a refined classification. The DSM is generated from the high resolution panchromatic images of a specific photo mission. Based on the aerial triangulation using area and feature-based points of interest the algorithms are able to generate a dense DSM by a dense image matching procedure. Afterwards a true ortho image for classification, panchromatic or color input images can be computed.In a last step specific layers for buildings and vegetation are generated and the classification is updated.     

Evaluation of Goddard’s LiDAR,hyperspectral, and thermal data products for mapping urban land-cover types

Goddard’s LiDAR (Light Detection And Ranging), hyperspectral and thermal (G-LiHT) airborne imager is a new system to advance concepts of data fusion for worldwide applications. A recent G-LiHT mission conducted in June 2016 over an urban area opens a new opportunity to assess the G-LiHT products for urban land-cover mapping. In this study, the G-LiHT hyperspectral and LiDAR-canopy height model (LiDAR-CHM) products were evaluated to map five broad land-cover types. A feature/decision-level fusion strategy was developed to integrate two products. Contemporary data processing techniques were applied, including object-based image analysis, machine-learning algorithms, and ensemble analysis. Evaluation focused on the capability of G-LiHT hyperspectral products compared with multispectral data with similar spatial resolution, the contribution of LiDAR-CHM, and the potential of ensemble analysis in land-cover mapping. The results showed that there was no significant difference between the application of the G-LiHT hyperspectral product and simulated Quickbird data in the classification. A synthesis of G-LiHT hyperspectral and LiDAR-CHM products achieved the best result with an overall accuracy of 96.3% and a Kappa value of 0.95 when ensemble analysis was applied. Ensemble analysis of the three classifiers not only increased the classification accuracy but also generated an uncertainty map to show regions with a robust classification as well as areas where classification errors were most likely to occur. Ensemble analysis is a promising tool for land-cover classification.     

高分辨率遥感影像解译中的机器学习范式

高分辨率遥感影像解译是遥感信息处理领域的研究热点之一,在遥感大数据知识挖掘与智能化分析中起着至关重要的作用,具有重要的民用和军事应用价值.传统的高分辨率遥感影像解译通常采用人工目视解译方式,费时费力且精度低.所以,如何自动、高效地实现高分辨率遥感影像解译是亟待解决的问题.近年来,随着人工智能技术的飞速发展,采用机器学习...     

基于GIS的中国东北植被综合分类研究

ＮＯＡＡ／ＡＶＨＲＲ由于运行周期短、覆盖范围大、成本低、波段宽等特点,目前正越来越广泛地受到人们的普遍关注。在大尺度、中尺度植被遥感上,ＮＯＡＡ／ＡＶＨＲＲ具有陆地卫星无法比拟的优势,但在另一方面,ＮＯＡＡＡＶＨＲＲ也存在分辨率低、数据变形较大和几何畸变较严重等问题。这样,在应用ＮＯＡＡＡＶＨＲＲ数据进行大区域植被制图时,植被分类的精度仍待提高。本文从理论上探讨了将地理信息系统提供的地理数据与遥感数据复合的可行性;尝试在ＧＩＳ环境下,将气温、降水、高程３个影响区域植被覆盖的主要指标,按一定的地面网格系统和数学模式进行量化,生成数字地学影像,并使之与经过优化、压缩处理的ＮＯＡＡＡＶＨＲＲ数据进行复合,对复合后的综合影像进行监督分类。分类结果显示,与传统的应用最大似然分类方法对单一遥感图像分类相比,该综合分类方法分类精度提高了１８．３％,该研究方法改变了遥感影像的单一信息结构;丰富了图像的信息含量;完成了地理数据的数字传输、处理、存储及影像化显示。     

TRMAP数字地图处理系统设计与开发

介绍了由陕西天润科技有限责任公司研究开发的数字地图处理系统TRMAP。该系统基于MicroStation平台开发,是为数字地图（DLG）生产和GIS数据采集、建库、更新以及内外业一体化成图设计的专用系统。简要阐述了其高效性和易用性的设计理念以及系统绘图、编辑、智能化的检查功能。     

GIS数据在专题地图可视化表达中的应用

以GIS数据与地图表达的关系为线索,结合林业规划制图过程,分析了GIS数据与地图表达的冲突,提出通过数据选择系统、分析与制图一体化策略及地图表现层次分类系统的构建来实现GIS数据与地图表达的融合,提高专题地图制图质量。     

Mapping natural and urban environments using airborne multi-sensor ADS40–MIVIS–LiDAR synergies

The recent and forthcoming availability of high spatial resolution imagery from satellite and airborne sensors offers the possibility to generate an increasing number of remote sensing products and opens new promising opportunities for multi-sensor classification. Data fusion strategies, applied to modern airborne Earth observation systems, including hyperspectral MIVIS, color-infrared ADS40, and LiDAR sensors, are explored in this paper for fine-scale mapping of heterogeneous urban/rural landscapes. An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection). The analysis focuses on the identification of the best performing data fusion configuration and investigates sensor-derived marginal improvements. Numerical experiments, performed on a 20-km stretch of the Marecchia River (Italy), allow for a quantification of the synergies of multi-sensor airborne data. The use of Maximum Likelihood and of the feature space including ADS40, LiDAR derived normalized digital surface, texture layers, and 24 MIVIS bands represents the scheme that maximizes the classification accuracy on the test set. The best classification provides high accuracy (92.57% overall accuracy) and demonstrates the potential of the proposed approach to define the optimized data fusion and to capture the high spatial variability of natural and human-dominated environments. Significant inter-class differences in the identification schemes are also found by indicating possible sub-optimal solutions for landscape-driven mapping, such as mixed forest, floodplain, urban, and agricultural zones.     

An Evaluation of Four Different Wavelet Decomposition Procedures for Spatial Feature Discrimination in Urban Areas

Texture or spatial arrangement of neighborhood objects and features plays an important role in the human visual system for pattern recognition and image classification. The traditional spectral–based image processing techniques have proven inadequate for urban land use and land cover mapping from images acquired by the current generation of fine–resolution satellites. This is because of the high frequency spatial arrangements or complex nature of urban features. There is a need for an effective algorithm to digitally classify urban land use and land cover categories using high–resolution image data. Recent studies using wavelet transforms for texture analysis have generally reported better accuracy. Based on a high–resolution ATLAS image, this study illustrates four different wavelet decomposition procedures – the standard, horizontal, vertical, and diagonal decompositions – for urban land use and land cover feature extraction with the use of 33×33 pixel samples. The standard decomposition approach was found to be the most efficient approach in urban texture analysis and classification. For comparison purposes and to better evaluate the accuracy of wavelet approaches in image classification, spatial autocorrelation techniques (Moran's I and Geary's C ) and the spatial co–occurrence matrix method were also examined. The results suggest that the wavelet transform approach is superior to all other approaches.     

Evaluation of Landsat TM data for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.)

LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.)