scholarly journals DSM-to-LoD2: Spaceborne Stereo Digital Surface Model Refinement

2018 ◽  
Vol 10 (12) ◽  
pp. 1926 ◽  
Author(s):  
Ksenia Bittner ◽  
Pablo d’Angelo ◽  
Marco Körner ◽  
Peter Reinartz
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Laser Scanning ◽  
Missing Values ◽  
Least Square ◽  
Rapid Expansion ◽  
Surface Model ◽  
Digital Surface Model ◽  
Sensing Applications ◽  
3D Surface

A digital surface model (DSM) provides the geometry and structure of an urban environment with buildings being the most prominent objects in it. Built-up areas change with time due to the rapid expansion of cities. New buildings are being built, existing ones are expanded, and old buildings are torn down. As a result, 3D surface models can increase the understanding and explanation of complex urban scenarios. They are very useful in numerous fields of remote sensing applications, in tasks related to 3D reconstruction and city modeling, planning, visualization, disaster management, navigation, and decision-making, among others. DSMs are typically derived from various acquisition techniques, like photogrammetry, laser scanning, or synthetic aperture radar (SAR). The generation of DSMs from very high resolution optical stereo satellite imagery leads to high resolution DSMs which often suffer from mismatches, missing values, or blunders, resulting in coarse building shape representation. To overcome these problems, we propose a method for 3D surface model generation with refined building shapes to level of detail (LoD) 2 from stereo half-meter resolution satellite DSMs using deep learning techniques. Mainly, we train a conditional generative adversarial network (cGAN) with an objective function based on least square residuals to generate an accurate LoD2-like DSM with enhanced 3D object shapes directly from the noisy stereo DSM input. In addition, to achieve close to LoD2 shapes of buildings, we introduce a new approach to generate an artificial DSM with accurate and realistic building geometries from city geography markup language (CityGML) data, on which we later perform a training of the proposed cGAN architecture. The experimental results demonstrate the strong potential to create large-scale remote sensing elevation models where the buildings exhibit better-quality shapes and roof forms than just using the matching process. Moreover, the developed model is successfully applied to a different city that is unseen during the training to show its generalization capacity.

scholarly journals An Efficient Multi-stage Object-Based Classification to Extract Urban Building Footprints from HR Satellite Images

2021 ◽  
Vol 38 (1) ◽  
pp. 191-196
Author(s):  
Gopala Krishna VSSN Pendyala ◽  
Hemantha Kumar Kalluri ◽  
Venkateswara C. Rao
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Images ◽  
Airborne Lidar ◽  
Surface Model ◽  
Digital Surface Model ◽  
Multi Stage ◽  
Object Based ◽  
Normalised Difference Vegetation Index ◽  
Remote Sensing Satellite

Urban building information can be effectively extracted by applying object-based image segmentation and multi-stage thresholding on High Resolution (HR) remote sensing satellite imageries. This study provides the results obtained using this method on the images of Indian remote sensing satellite, CARTOSAT-2S launched by the Indian Space Research Organization (ISRO). In this study, a method is developed to extract urban building footprints from the HR remote sensing satellite images. The first step of the process consists of generating highly dense per pixel Digital Surface Model (DSM) by using semi global matching algorithm on HR satellite stereo images and applying robust ground filtering to generate Digital Terrain Model (DTM). In the second step, multi-stage object-based approach is adopted to extract building bases using the PAN sharpened image, normalized Digital Surface Model (nDSM) derived from DSM and DTM, and Normalised Difference Vegetation Index (NDVI). The results are compared with the manual method of drawing building footprints by cartographers. An average precision of 0.930, recall of 0.917, and f-score of 0.922 are obtained. The results are found to be in a match with the method using the high resolution Airborne LiDAR DSM by providing a solution for large areas, low cost and low time.

scholarly journals ORTHO-RECTIFICATION OF NARROW BAND MULTI-SPECTRAL IMAGERY ASSISTED BY DSLR RGB IMAGERY ACQUIRED BY A FIXED-WING UAS

2015 ◽  
Vol XL-1/W4 ◽  
pp. 67-74 ◽  
Author(s):  
J.-Y. Rau ◽  
J.-P. Jhan ◽  
C.-Y Huang
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Narrow Band ◽  
Surface Model ◽  
Digital Surface Model ◽  
Aerial Triangulation ◽  
Sensing Applications ◽  
Rgb Images ◽  
Remote Sensing Applications ◽  
Orientation Parameters

Miniature Multiple Camera Array (MiniMCA-12) is a frame-based multilens/multispectral sensor composed of 12 lenses with narrow band filters. Due to its small size and light weight, it is suitable to mount on an Unmanned Aerial System (UAS) for acquiring high spectral, spatial and temporal resolution imagery used in various remote sensing applications. However, due to its wavelength range is only 10 nm that results in low image resolution and signal-to-noise ratio which are not suitable for image matching and digital surface model (DSM) generation. In the meantime, the spectral correlation among all 12 bands of MiniMCA images are low, it is difficult to perform tie-point matching and aerial triangulation at the same time. In this study, we thus propose the use of a DSLR camera to assist automatic aerial triangulation of MiniMCA-12 imagery and to produce higher spatial resolution DSM for MiniMCA12 ortho-image generation. Depending on the maximum payload weight of the used UAS, these two kinds of sensors could be collected at the same time or individually. In this study, we adopt a fixed-wing UAS to carry a Canon EOS 5D Mark2 DSLR camera and a MiniMCA-12 multi-spectral camera. For the purpose to perform automatic aerial triangulation between a DSLR camera and the MiniMCA-12, we choose one master band from MiniMCA-12 whose spectral range has overlap with the DSLR camera. However, all lenses of MiniMCA-12 have different perspective centers and viewing angles, the original 12 channels have significant band misregistration effect. Thus, the first issue encountered is to reduce the band misregistration effect. Due to all 12 MiniMCA lenses being frame-based, their spatial offsets are smaller than 15 cm and all images are almost 98% overlapped, we thus propose a <b>modified projective transformation</b> (MPT) method together with two systematic error correction procedures to register all 12 bands of imagery on the same image space. It means that those 12 bands of images acquired at the same exposure time will have same interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) after band-to-band registration (BBR). Thus, in the aerial triangulation stage, the master band of MiniMCA-12 was treated as a reference channel to link with DSLR RGB images. It means, all reference images from the master band of MiniMCA-12 and all RGB images were triangulated at the same time with same coordinate system of ground control points (GCP). Due to the spatial resolution of RGB images is higher than the MiniMCA-12, the GCP can be marked on the RGB images only even they cannot be recognized on the MiniMCA images. Furthermore, a one meter gridded digital surface model (DSM) is created by the RGB images and applied to the MiniMCA imagery for ortho-rectification. Quantitative error analyses show that the proposed BBR scheme can achieve 0.33 pixels of average misregistration residuals length and the co-registration errors among 12 MiniMCA ortho-images and between MiniMCA and Canon RGB ortho-images are all less than 0.6 pixels. The experimental results demonstrate that the proposed method is robust, reliable and accurate for future remote sensing applications.

scholarly journals High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

2015 ◽  
Vol XL-7/W4 ◽  
pp. 197-201 ◽  
Author(s):  
Y. Yang ◽  
H. T. Li ◽  
Y. S. Han ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Graph Theory ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Sensing Applications ◽  
Initial Object ◽  
Object Layer ◽  
Evolution Approach

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

scholarly journals SAR AND OBLIQUE AERIAL OPTICAL IMAGE FUSION FOR URBAN AREA IMAGE SEGMENTATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 639-642 ◽  
Author(s):  
J. Fagir ◽  
A. Schubert ◽  
M. Frioud ◽  
D. Henke
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Single Channel ◽  
Research Area ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Surface Model ◽  
Digital Surface Model ◽  
Oblique View ◽  
Optical Images

The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform – leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

scholarly journals UAV AERIAL SURVEY: ACCURACY ESTIMATION FOR AUTOMATICALLY GENERATED DENSE DIGITAL SURFACE MODEL AND ORTHOTHOTO PLAN

2016 ◽  
Vol XLI-B6 ◽  
pp. 155-159
Author(s):  
M. A. Altyntsev ◽  
S. A. Arbuzov ◽  
R. A. Popov ◽  
G. V. Tsoi ◽  
M. O. Gromov
Keyword(s):  
Survey Data ◽  
Laser Scanning ◽  
Aerial Survey ◽  
Surface Model ◽  
Model Generation ◽  
Digital Surface Model ◽  
Reliable Criterion ◽  
Accuracy Estimation ◽  
Survey Accuracy ◽  
Digital Orthophoto

A dense digital surface model is one of the products generated by using UAV aerial survey data. Today more and more specialized software are supplied with modules for generating such kind of models. The procedure for dense digital model generation can be completely or partly automated. Due to the lack of reliable criterion of accuracy estimation it is rather complicated to judge the generation validity of such models. One of such criterion can be mobile laser scanning data as a source for the detailed accuracy estimation of the dense digital surface model generation. These data may be also used to estimate the accuracy of digital orthophoto plans created by using UAV aerial survey data. The results of accuracy estimation for both kinds of products are presented in the paper.

scholarly journals Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data

2019 ◽  
Vol 11 (15) ◽  
pp. 1812 ◽  
Author(s):  
Jonathan P. Dash ◽  
Michael S. Watt ◽  
Thomas S. H. Paul ◽  
Justin Morgenroth ◽  
Grant D. Pearse
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Laser Scanning ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Validation Dataset ◽  
Height Range ◽  
Exotic Trees ◽  
Invasive Trees ◽  
Invasive Exotic

Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand’s South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa < 0.996 ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants.

scholarly journals Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

2017 ◽  
Vol 21 (11) ◽  
pp. 5693-5708 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Tiphaine Tallec ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model included in the EXternalized SURface (SURFEX) modeling platform. The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km2 (24 km  ×  24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

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