Stereo Mapping Research Articles

The study of high resolution stereo mapping satellite

The study of high resolution stereo mapping satellite

高分七号双线阵立体测绘相机系统集成与测试

高分七号双线阵立体测绘相机系统集成与测试

高分七号双线阵立体测绘相机系统集成与测试

高分七号双线阵立体测绘相机系统集成与测试

UPDATES OF ‘AW3D30’ ALOS GLOBAL DIGITAL SURFACE MODEL WITH OTHER OPEN ACCESS DATASETS

Abstract. In 2016 we first completed the global data processing of digital surface models (DSMs) by using the whole archives of stereo imageries derived from the Panchromatic Remote sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS). The dataset was freely released to the public in 30 m grid spacing as the ‘ALOS World 3D - 30m (AW3D30)’, which was generated from its original version processed in 5 m or 2.5 m grid spacing. The dataset has been updated since then to improve the absolute/relative height accuracies with additional calibrations. However the most significant update that should be applied for improving the data usability is the filling of void areas, which correspond to approx. 10% of global coverage, mostly due to cloud covers. In this paper we introduce the updates of AW3D30 filling the voids with other open-access DSMs such as Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), ArcticDEM, etc., through inter-comparisons among these datasets.

GENERAL DESIGN OF SPACE OPTICAL MAPPING REMOTE SENSING CAMERA

Abstract. Surveying and mapping plays an important role in all aspects of economic development, national infrastructure construction and national defence construction. Especially in the overall and strategic level, most of them need to apply geospatial information. Geospatial information acquired through surveying and mapping technology has become an important strategic resource. Because space photography is faster, cheaper and unrestricted by regions and borders, countries around the world are scrambling to develop their own mapping satellites.In recent years, great breakthrough has been made in the field of on-board optical stereo mapping, many countries have launched satellites with surveying and mapping as the main function, and obtained image data from 1:50000 to 1:10000 scale. This paper describes a design of a stereo surveying and mapping camera system that can achieve 1:10000 scale maps.

Topographic Analysis of Landslide Distribution Using AW3D30 Data

Landslides cause serious damage to society, and some occur as reactivations of old landslides in response to earthquakes and/or rainfall. Landslide distributions are therefore useful when siting engineering projects such as road and tunnel constructions. Although several methods have been proposed to extract landslides from topographic data on the basis of their morphological features (crown, main scarp, and main body), such morphological features are gradually eroded by heavy precipitation or landslide recurrence. Therefore, conventional methods cannot always identify areas influenced by recurrent landslides. In this study, we investigated the relationship between ridgeline continuity and landslide distribution using AW3D30, which is a global digital surface model (DSM) dataset produced from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) launched by the Japan Aerospace Exploration Agency (JAXA) in 2013. The relationship between the area of landslides and the number of ridge pixels was analyzed, and we propose a method for estimating the upper bound distribution of landslide topographies based on extracted ridgelines data using the Data Envelopment Analysis (DEA) function on the R statistical software packages. The upper bound on the area of landslides decreases as the number of ridge pixels increases. The same trend was seen in all the five sites, and the upper bound derived from one site is hardly exceeded by those derived from all other sites. By using the upper bound distribution function, the landslide distribution will not be missed.

Integrated geometric self-calibration of stereo cameras onboard the ZiYuan-3 satellite

The geometric quality of images is a principal concern for a stereo mapping satellite. On-orbit geometric calibration (GC) is an essential processing step that is always performed to ensure the geometric accuracy of satellite images. However, the traditional GC method is typically expensive and inefficient due to a large dependence on ground-based locations. Although recent GC methods based on relative constraints between images can substantially reduce plane constraints, which are used as the benchmark measurements in the traditional method, there is a strong correlation between elevation and camera orientation that necessitates additional elevation constraints. This paper proposes an integrated GC method to compensate for systematic errors in the three linear cameras (TLC) of the ZiYuan-3 (ZY-3) satellite. The relative constraints and the stereo intersection constraints from two sets of overlapped TLC imagery pairs allow for the removal of dense plane constraints, as well as elimination of the GC dependencies from the additional elevation constraints. This paper also systematically analyzes the relationship between elevation error and camera GC accuracy, proposes a unified GC model for TLC cameras based on matrix decomposition, and develops a suitable global estimation method for the solution of GC parameters. The method was validated through experiments performed on the TLC images, and satisfactory results indicated that it effectively compensates for systematic errors by onboard stereo TLC cameras.

Vibration model of Ziyuan3 satellites considering frequency changing

Ziyuan3-01 and Ziyuan3-02, Chinese civilian high-resolution stereo mapping satellites, were designed to satisfy the requirements of 1:50,000 digital topographical mapping. Platform vibration detection plays an important role in the Ziyuan3 satellite project. Various methods have been used to detect the vibration of Ziyuan3-01, but none focuses on that the vibration frequency is not a fixed value. To detect and model the vibration, this study proposes a method based on a set of raw gyroscope data to detect long-term vibration and model it with considering frequency changing. Two experiments were conducted, and the results showed that (a) the proposed method can detect the long-term vibration frequency and model it; (b) there is a same vibration frequency of approximately 0.65 Hz in the Ziyuan3-01 and Ziyuan3-02; (c) the 0.65 Hz vibration suffered by the Ziyuan3 satellite series is not a fixed value, varying from 0.59 Hz to 0.7 Hz, and the period of the vibration change is a constant value of approximately 1 year, which we modeled with a sine function; and (d) the working state of loads on the platform, such as solar panels, has an influence on the vibration frequency certainly.

Design and Data Processing of China's First Spaceborne Laser Altimeter System for Earth Observation: GaoFen-7

The GaoFen-7 (GF-7) satellite, which was launched on November 3, 2019, is China's first civilian submeter stereo mapping satellite. The satellite is equipped with the first laser altimeter officially in China for earth observation. Except for the laser altimeter, the GF-7 spaceborne laser altimeter system also includes two laser footprint cameras and a laser optical axis surveillance camera. The laser altimeter system is designed and used to assist improving the elevation accuracy without ground control points of the two line-array stereo mapping cameras. This article details the design of the GF-7 spaceborne laser altimeter system, its ranging performance in the laboratory, and its data processing method. The type of data products is also released. These data will play a vital role in the application of geography, glaciology, forestry, and other industries.

Research on the Control Point Extraction Method of Chinese Gaofen 7 Satellite Using Altimeter and Footprint Camera

Satellite laser altimetry is one of the most advanced information acquisition technologies in Earth observation system. It can provide high-accuracy elevation information, however, due to the lack of detail intensity information, its planimetric accuracy is usually worse than the elevation accuracy. Gaofen 7 (GF-7) satellite scheduled for launch in 2019 will be equipped with laser altimeter, footprint camera, stereo mapping camera, etc. The laser altimeter together with the footprint camera was designed to provide high accuracy ground control point of satellite mapping. The laser altimeter can provide the high-accuracy elevation information and the joint processing of footprint camera and stereo mapping camera can provide high-accuracy planimetric information. Therefore, this paper mainly studies the technology of extracting high-accuracy control points based on GF-7 satellite’s altimeter, footprint camera and stereo mapping camera using a simulated dataset extracted from Quickbird image and ICESat altimetric data.

Feature‐assisted stereo correlation

AbstractStereo correlation in digital image correlation (DIC) involves an optimisation problem that is sensitive to initial guess. In practice, this problem is circumvented by manually selecting a pair of points in the two stereo images that guarantees convergence and provides stereo mapping parameter estimates that are used as initial guesses at neighbouring subsets. However, such an approach is not always feasible, especially in the presence of substantial perspective distortions, for example, due to large stereo angles or complexities in specimen geometry. Therefore, it is desirable to provide high‐quality independent initial estimates over the entire region of interest. Recently, SIFT has been used for this purpose, but it fails when perspective distortions are severe. In this work, we investigate seven other feature‐based matching techniques to address this gap. Among these, DeepFlow algorithm provides the highest quality and most spatially uniform initial estimates. Further, we use DeepFlow estimates as initial guesses in a conventional stereo optimisation to compute geometry measures of a specimen in DIC challenge dataset. These geometry measures show excellent agreement with ground truth, further supporting the choice of DeepFlow in stereo correlation.

UFPR CampusMap: a laboratory for a Smart City developments

UFPR CampusMap: a laboratory for a Smart City developments

Gait metric learning siamese network exploiting dual of spatio-temporal 3D-CNN intra and LSTM based inter gait-cycle-segment features

Gait recognition is a non-invasive biometric technology that can be used to identify humans in surveillance systems. It is based on the style or manner in which a person walk and can be realized with minimal amount of individual cooperation for its acquisition. However, it may causes many challenges in the form of varying viewpoints, carrying conditions and clothing variations. To tackle such limitations, we present a view-invariant gait recognition network that divide the gait cycle into five segments (GCS). The intra gait-cycle-segment (GCS) convolutional spatio-temporal relationships has been obtained by employing a 3D-CNN via. transfer learning mechanism. Later, a stacked LSTM has been trained over spatio-temporal features to learn the long and short relationship between inter gait-cycle-segment.The first step in our work is data pre-processing, in which we create silhouette stereo map (SSM) from the binary silhouettes of the gait video frame and sampled each video into a fixed 80 frames. These 80 frames SSM have been divided into 5 gait-cycle-segments (GCS) of 16 frames each. From each of these GCS, we extract spatio-temporal features using a pre-trained 3-D CNN. These features have been concatenated temporally, and an LSTM cell is used to learn the long-term dependencies between each GCS. Finally, the required class scores are computed by averaging (to handle noise) the output generated by LSTM. The network is trained in an end-to-end fashion using triplet loss function so as to learn the gait metric well using only the hard triplets. All the experiments are carried out on publicly available CASIA-B and OU-ISIR gait dataset. From the experimental results, it has been indicated that the proposed network performs better than the current state-of-the-art gait recognition systems.

Dimension reduction of multi-temporal elevation data

Near-global digital elevation models (DEMs) with a horizontal resolution 1 arc sec, from the Shuttle Radar Topography Mission (SRTM DEM), the Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER DEM) sensor, and the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board the Advanced Land Observing Satellite (ALOS DEM) are available. These three elevation products present differences in the elevation conceptual framework resulting from noise and error effects as well as to changes in landcover and geomorphology of earth surface. The aim of this research effort is to highlight the differences among these three DEMS. Towards this end, a method is presented that is capable of decomposing the multi-dimensional elevation data formed by these 3 DEMs to two components: (a) the first one (adjusted DEMs) includes the mostly similar/common information among the initial DEMs, while (b) the second one (residual DEMs) maximizes the differences among the data. Adjusted DEMs might be used as first-degree “optimal” terrain representations while the residual DEMs are suitable to outline and map regions of high- and low-elevation variability among the three DEMs under comparison, due to (a) errors, (b) differences in the elevation conceptual framework among the three DEMs, and (c) landcover change over time.

REAL-TIME ON-ORBIT CALIBRATION OF ANGLES BETWEEN STAR SENSOR AND EARTH OBSERVATION CAMERA FOR OPTICAL SURVEYING AND MAPPING SATELLITES

Abstract. On space remote sensing stereo mapping field, the angle variation between the star sensor’s optical axis and the earth observation camera’s optical axis on-orbit affects the positioning accuracy, when optical mapping is without ground control points (GCPs). This work analyses the formation factors and elimination methods for both the star sensor’s error and the angles error between the star sensor’s optical axis and the earth observation camera’s optical axis. Based on that, to improve the low attitude stability and long calibration time necessary of current satellite cameras, a method is then proposed for real-time on-orbit calibration of the angles between star sensor’s optical axis and the earth observation camera’s optical axis based on the principle of auto-collimation. This method is experimentally verified to realize real-time on-orbit autonomous calibration of the angles between the star sensor’s optical axis and the earth observation camera’s optical axis.

Mapping Canopy Heights of Poplar Plantations in Plain Areas Using ZY3-02 Stereo and Multispectral Data

Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.

An Adaptive Multifeature Method for Semiautomatic Road Extraction From High-Resolution Stereo Mapping Satellite Images

In this letter, an adaptive multifeature method for semiautomatic road extraction in high-resolution stereo mapping satellite images is proposed. First, the digital surface model (DSM) is generated from high-resolution stereo mapping satellite images by the semiglobal vertical line locus matching method. To combine the entropy feature and the spectral feature with the DSM, an adaptive method based on the image matching theory is introduced, which maintains a dynamic balance in the road extraction process and ensures the complementarity of each feature. Then, the geodesic road tracking method integrated with kernel density estimation and mean shift is used to extract roads from the feature fusion map. Experimental results have shown that the proposed method can extract roads both smoothly and correctly from high-resolution stereo mapping satellite images and especially performs better than other existing methods when there is an elevation difference between the road target and its neighborhood.

Pixel-Based Geometric Assessment of Channel Networks/Orders Derived from Global Spaceborne Digital Elevation Models

Digital Elevation Models (DEMs) contribute to geomorphological and hydrological applications. DEMs can be derived using different remote sensing-based datasets, such as Interferometric Synthetic Aperture Radar (InSAR) (e.g., Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) and Shuttle Radar Topography Mission (SRTM) DEMs). In addition, there is also the Digital Surface Model (DSM) derived from optical tri-stereo ALOS Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) imagery. In this study, we evaluated satellite-based DEMs, SRTM (Global) GL1 DEM V003 28.5 m, ALOS DSM 28.5 m, and PALSAR DEMs 12.5 m and 28.5 m, and their derived channel networks/orders. We carried out these assessments using Light Detection and Ranging (LiDAR) Digital Surface Models (DSMs) and Digital Terrain Models (DTMs) and their derived channel networks and Strahler orders as reference datasets at comparable spatial resolutions. We introduced a pixel-based method for the quantitative horizontal evaluation of the channel networks and Strahler orders derived from global DEMs utilizing confusion matrices at different flow accumulation area thresholds (ATs) and pixel buffer tolerance values (PBTVs) in both ±X and ±Y directions. A new Python toolbox for ArcGIS was developed to automate the introduced method. A set of evaluation metrics—(i) producer accuracy (PA), (ii) user accuracy (UA), (iii) F-score (F), and (iv) Cohen’s kappa index (KI)—were computed to evaluate the accuracy of the horizontal matching between channel networks/orders extracted from global DEMs and those derived from LiDAR DTMs and DSMs. PALSAR DEM 12.5 m ranked first among the other global DEMs with the lowest root mean square error (RMSE) and mean difference (MD) values of 4.57 m and 0.78 m, respectively, when compared to the LiDAR DTM 12.5 m. The ALOS DSM 28.5 m had the highest vertical accuracy with the lowest recorded RMSE and MD values of 4.01 m and −0.29 m, respectively, when compared to the LiDAR DSM 28.5 m. PALSAR DEM 12.5 m and ALOS DSM 28.5 m-derived channel networks/orders yielded the highest horizontal accuracy when compared to those delineated from LiDAR DTM 12.5 m and LiDAR DSM 28.5 m, respectively. The number of unmatched channels decreased when the PBTV increased from 0 to 3 pixels using different ATs.

Perceptually Lossless Compression for Mastcam Multispectral Images: A Comparative Study

The two mast cameras, Mastcams, onboard Mars rover Curiosity are multispectral imagers with nine bands in each. Currently, the images are compressed losslessly using JPEG, which can achieve only two to three times of compression. We present a comparative study of four approaches to compressing multispectral Mastcam images. The first approach is to divide the nine bands into three groups with each group having three bands. Since the multispectral bands have strong correlation, we treat the three groups of images as video frames. We call this approach the Video approach. The second approach is to compress each group separately and we call it the split band (SB) approach. The third one is to apply a two-step approach in which the first step uses principal component analysis (PCA) to compress a nine-band image cube to six bands and a second step compresses the six PCA bands using conventional codecs. The fourth one is to apply PCA only. In addition, we also present subjective and objective assessment results for compressing RGB images because RGB images have been used for stereo and disparity map generation. Five well-known compression codecs, including JPEG, JPEG-2000 (J2K), X264, X265, and Daala in the literature, have been applied and compared in each approach. The performance of different algorithms was assessed using four well-known performance metrics. Two are conventional and another two are known to have good correlation with human perception. Extensive experiments using actual Mastcam images have been performed to demonstrate the various approaches. We observed that perceptually lossless compression can be achieved at 10:1 compression ratio. In particular, the performance gain of the SB approach with Daala is at least 5 dBs in terms peak signal-to-noise ratio (PSNR) at 10:1 compression ratio over that of JPEG. Subjective comparisons also corroborated with the objective metrics in that perceptually lossless compression can be achieved even at 20 to 1 compression.

Quantifying mass balance of East-Karakoram glaciers using geodetic technique

The Karakoram Range comprises the heavily glaciated mountains outside the Polar regions. A comprehensive assessment of glaciers variation in this region is crucial to ascertain constant water supply under different scenarios of futuristic climate change. Lack of accessibility to the Karakoram region has limited the use of conventional methods in monitoring variations in thickness and mass of glaciers, alternatively promoting the use of remote sensing based methods. The present study quantifies changes in thickness and mass budget of 24 glaciers in East-Karakoram region using geodetic technique. The Digital Elevation Model (DEM) adjustments and comparisons were made between Shuttle Radar Topography Mission (SRTM) C-band DEM, year 2000 and recent DEMs of the years 2009 and 2014, which were generated using stereo pairs of Panchromatic Remote Sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observing Satellite (ALOS) and Cartosat-1 respectively. The vertical accuracy of recent DEMs was assessed using ICESat-1 laser points. Other biases such as elevation dependent, radar penetration, terrain curvature and seasonality were also rectified before computing the glacier mass balance. Geodetic esimates indicate an overall marginal thinning (−0.09 ± 0.16 m/yr) and negative mass budget (−0.08 ± 0.13 m.w.e./yr) during the years 2000–2014. Interestingly, one glacier showed surge-type activity (advancing and thickening terminus) during 2000-2014, with highest (0.59) Accumulation Area Ratio (AAR) amongst studied glaciers. Time series analysis of climate data (temperature and precipitation) recorded at field observatories depict overall warming in the East-Karakoram region. The higher rate of mass loss during 2009–2014 (−0.07 ± 0.13 m.w.e./yr) compared to 2000–2009 (−0.05 ± 0.12 m.w.e./yr) was found in agreement with the rise in temperature and decline in precipitation over the region during different years. The results thus indicate an insignificant mass loss in East-Karakoram glaciers and thus exhibit a nearly balanced state since the year 2000 in contrast to glacier mass loss in other Himalayan ranges.