High-resolution Satellite Stereo Images Research Articles

Integration of very high-resolution stereo satellite images and airborne or satellite Lidar for Eucalyptus canopy height estimation

Eucalyptus plantations cover extensive areas in tropical regions and require accurate growth monitoring for efficient management. Traditional in-situ measurements, while necessary, are labor-intensive and impractical for large-scale assessments. Very high-resolution satellite stereo imagery is playing an increasingly important role in the estimation of fine Digital Surface Models (DSMs) across landscapes. However, its ability to estimate canopy height models (CHMs) has not been widely investigated. This study investigates the integration of high-resolution satellite stereo imagery from the Pleiades sensor with airborne or satellite Lidar data to estimate canopy height over eucalyptus plantations. Two study sites were selected in Brazil, representing flat and semi-mountainous topographies, Mato Grosso do Sul (MS) and Sao Paulo (SP), respectively. Digital Surface Models generated from Pleiades images (DSMP) were combined with Digital Terrain Models extracted from airborne Lidar data (DTMALS) to create Canopy Height Models (CHMALS). The evaluation of the CHMALS was based on two in situ canopy height measurements (Hmax and Hmean). For the SP site, the CHMALSmax, which is the average height of top 10% pixel values within each plot, correlated well with in situ Hmean, which is the average height of 10 central trees (r = 0.98), showing a bias of 1.4 m, RMSE of 3.1 m, and rRMSE of 18.5%. At the MS site, CHMALSmax demonstrated a bias of 1.9 m, RMSE of 2.3 m, rRMSE of 17.3%, and r correlation of 0.92. Despite a tendency to underestimate heights below 20 m in young tree plantations with open canopy, the results indicate reliable canopy height estimation. The study also investigates the potential of Global Ecosystem Dynamics Investigation (GEDI) elevation data as an alternative to DTMALS in absence of airborne Lidar data. The resulting CHMGedi is promising but slightly less accurate than Lidar-based CHMs. The best GEDI-based CHM (CHMGedimax) showed a bias and rRMSE of 1.3 m and 20.5% for the SP site, and 2.2 m and 24.9% for the MS site. These findings highlight the potential for integrating Pleiades and Lidar data for efficient and accurate canopy height monitoring in eucalyptus plantations.

Towards accurate binocular vision of satellites: A cascaded multi-scale pyramid network for stereo matching on satellite imagery

Stereo matching of high-resolution satellite stereo images is a fundamental and challenging task in photogrammetry, geoscience, and remote sensing. Some deep learning-based methods have shown great potential in solving this task recently. However, these methods have not attracted much attention as their accuracy is still unsatisfactory, especially in regions with intractable factors (e.g., occlusions, repetitive patterns, low texture, non-texture, and disparity discontinuities). To tackle these challenging factors and improve disparity accuracy, we propose an end-to-end stereo matching network named Cascaded Multi-Scale Pyramid Network (CMSP-Net). Firstly, to fully utilize the multi-level spatial context information and mitigate the ambiguous matching caused by repetitive patterns, low texture, non-texture, and occlusions, a cost volume pyramid is constructed with multi-scale image features. Then, each cost volume in the pyramid is aggregated with an hourglass structure to exploit the multi-scale context in a single cost volume. Secondly, considering that high-resolution cost volumes contain more detailed information but have small receptive fields, while low-resolution ones have large receptive fields but lack details, we design an Attention-Guided Cost Fusion (AGCF) strategy to fuse pyramid cost volumes, which effectively integrates the advantages of multi-scale cost volumes while mitigating the backfiring caused by semantic inconsistency across different scales. Thirdly, a disparity refinement module is designed to improve overall disparity accuracy further, which incorporates the reconstruction error and edge clues of input satellite stereo images. Extensive experimental results on multiple satellite datasets demonstrate that the proposed method can effectively reduce matching ambiguities and recover sharp disparity discontinuities, achieving clear superiority over existing state-of-the-art methods.

Building damage assessment using improved combined adjustment and automatic building corners extraction method

ABSTRACTAccurately estimating the collapse height of buildings during earthquakes holds great significance. Acquiring precise height information of buildings using dual-phase high-resolution stereo satellite images (HRSSIs) requires high-precision ground control points, which are difficult to obtain in earthquake-stricken areas. Spaceborne laser data has been validated to significantly improve the vertical accuracy of HRSSIs. Therefore, this paper first proposes the application of a combined stereo adjustment model in building collapse estimation and thoroughly verifies its feasibility. Firstly, the laser altimetry points (LAPs) acquired by GF-7 and ICESat-1 serve as elevation control points (ECPs), and the tie points from HRSSIs were used as constraint to conduct combined adjustment to improve the consistency of stereoscopic positioning accuracy between HRSSIs before and after the earthquake. Subsequently, a footprints-to-conners, coarse-to-fine, automatic building corners extraction method was performed. Avoiding the time-consuming generation of large-scale DSM while also improving the accuracy of building collapse assessment. Finally, according to the height change of building corners calculated from HRSSIs, building damage assessment was completed. According to the above methods, independent experiments were carried out respectively for the Oaxaca earthquake in Mexico in 2020 and the Kahramanmaras earthquake in Türkiye in 2023. GF-7 HRSSIs was used to verify the accuracy and stability of the proposed method for 3D assessment of building damage. The results indicate that the RMSE of elevation positioning accuracy has been greatly reduced in both regions, ranging from 10.29 m to 0.95 m and from 6.67 m to 0.99 m, respectively. This accuracy guarantees the feasibility of detecting the building floor-level collapse. And 2541 building corner points were extracted from two disaster areas, with correct corner points accounting for 85.18%. A total of 520 buildings were correctly extracted corner points, with an accuracy rate of 94.12% for assessing the degree of damage.

HMSM-Net: Hierarchical multi-scale matching network for disparity estimation of high-resolution satellite stereo images

Disparity estimation of satellite stereo images is an essential and challenging task in photogrammetry and remote sensing. Recent researches have greatly promoted the development of disparity estimation algorithms by using CNN (Convolutional Neural Networks) based deep learning techniques. However, it is still difficult to handle intractable regions that are mainly caused by occlusions, disparity discontinuities, texture-less areas, and repetitive patterns. Besides, the lack of training datasets for satellite stereo images remains another major issue that blocks the usage of CNN techniques due to the difficulty of obtaining ground-truth disparities. In this paper, we propose an end-to-end disparity learning model, termed hierarchical multi-scale matching network (HMSM-Net), for the disparity estimation of high-resolution satellite stereo images. First, multi-scale cost volumes are constructed by using pyramidal features that capture spatial information of multiple levels, which learn correspondences at multiple scales and enable HMSM-Net to be more robust in intractable regions. Second, stereo matching is executed in a hierarchical coarse-to-fine manner by applying supervision to each scale, which allows a lower scale to act as prior knowledge and guides a higher scale to attain finer matching results. Third, a refinement module that incorporates the intensity and gradient information of the input left image is designed to regress a detailed full-resolution disparity map for local structure preservation. For network training and testing, a dense stereo matching dataset is created and published by using GaoFen-7 satellite stereo images. Finally, the proposed network is evaluated on the Urban Semantic 3D and GaoFen-7 datasets. Experimental results demonstrate that HMSM-Net achieves superior accuracy compared with state-of-the-art methods, and the improvement on intractable regions is noteworthy. Additionally, results and comparisons of different methods on the GaoFen-7 dataset show that it can severs as a challenging benchmark for performance assessment of methods applied to disparity estimation of satellite stereo images. The source codes and evaluation dataset are made publicly available at https://github.com/Sheng029/HMSM-Net.

Building Change Detection Based on 3D Co-Segmentation Using Satellite Stereo Imagery

Building change detection using remote sensing images is significant to urban planning and city monitoring. The height information extracted from very high resolution (VHR) satellite stereo images provides valuable information for the detection of 3D changes in urban buildings. However, most existing 3D change detection algorithms are based on the independent segmentation of two-temporal images and the feature fusion of spectral change and height change. These methods do not consider 3D change information and spatial context information simultaneously. In this paper, we propose a novel building change detection algorithm based on 3D Co-segmentation, which makes full use of the 3D change information contained in the stereoscope data. An energy function containing spectral change information, height change information, and spatial context information is constructed. Image change feature is extracted using morphological building index (MBI), and height change feature is obtained by robust normalized digital surface models (nDSM) difference. 3D Co-segmentation divides the two-temporal images into the changed foreground and unchanged background through the graph-cut-based energy minimization method. The object-to-object detection results are obtained through overlay analysis, and the quantitative height change values are calculated according to this correspondence. The superiority of the proposed algorithm is that it can obtain the changes of buildings in planar and vertical simultaneously. The performance of the algorithm is evaluated in detail using six groups of satellite datasets. The experimental results prove the effectiveness of the proposed building change detection algorithm.

High-Resolution Satellite Stereo Matching by Object-Based Semiglobal Matching and Iterative Guided Edge-Preserving Filter

This letter presents an object-based stereo matching and content-based guided disparity map refinement for high-resolution satellite stereo images. The proposed method uses the superpixels for object-based cost filtering and then object-based semiglobal matching. In the proposed object-based stereo matching strategy, two improvements including homogeneity weight for cost filtering, and weighted cost aggregation by image objects are considered. After cost aggregation, the generated disparity map is computed using the winner takes all and then refined with a new iterative guided edge-preserving filter. The proposed method with Census cost function has been implemented on high-resolution satellite stereo images and then is compared with the LiDAR ground truth. Moreover, the proposed method is compared with five state-of-the-art stereo matching methods. The experimental results on along-track satellite stereo images from Pleiades and IKONOS images and cross-track multidate images from WV-III demonstrate that the proposed method significantly improves the result of stereo matching.

Object-based modelling and accuracy assessment of Gordion tumuli using very high-resolution stereo satellite images

ABSTRACT Very high-resolution (VHR) stereo satellite images provide detailed spatial and spectral information for detecting and monitoring artificial or natural objects. This information can also be used for object-based change detection. This study aims to reveal the potential of three-dimensional (3D) information generated from VHR stereo satellite images to determine the physical changes that may occur on Gordion tumuli. For this purpose, point cloud data were generated from WorldView-3 and KOMPSAT-3 stereo satellite images. The accuracy of these datasets was tested using 161 check points (CPs). On the other hand, digital surface models (DSMs) generated from VHR stereo satellite images were used for object-based modelling of tumuli. The vertical accuracy of these DSMs was tested by comparing them with reference DSMs extracted from Unmanned Aerial System (UAS) images. As a result of the vertical accuracy assessment of the point cloud data, 0.16 m and 0.53 m root mean square error (RMSE) values were obtained for WorldView-3 and KOMPSAT-3 data, respectively. The average RMSE values obtained as a result of the model-based comparison of DSMs are 0.28 m for WorldView-3 and 0.57 m for KOMPSAT-3. These results demonstrate that the sub-metre level physical changes occurring on tumuli can be determined by VHR stereo satellite images.

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

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.

Detection of Parking Cars in Stereo Satellite Images

In this paper, we present a Remote Sens. approach to localize parking cars in a city in order to enable the development of parking space availability models. We propose to use high-resolution stereo satellite images for this problem, as they provide enough details to make individual cars recognizable and the time interval between the stereo shots allows to reason about the moving or static condition of a car. Consequently, we describe a complete processing pipeline where raw satellite images are georeferenced, ortho-rectified, equipped with a digital surface model and an inclusion layer generated from Open Street Model vector data, and finally analyzed for parking cars by means of an adapted Faster R-CNN oriented bounding box detector. As a test site for the proposed approach, a new publicly available dataset of the city of Barcelona labeled with parking cars is presented. On this dataset, a Faster R-CNN model directly trained on the two ortho-rectified stereo images achieves an average precision of 0.65 for parking car detection. Finally, an extensive empirical and analytical evaluation shows the validity of our idea, as parking space occupancy can be broadly derived in fully visible areas, whereas moving cars are efficiently ruled out. Our evaluation also includes an in-depth analysis of the stereo occlusion problem in view of our application scenario as well as the suitability of using a reconstructed Digital Surface Model (DSM) as additional data modality for car detection. While an additional adoption of the DSM in our pipeline does not provide a beneficial cue for the detection task, the stereo images provide essentially two views of the dynamic scene at different timestamps. Therefore, for future studies, we recommend a satellite image acquisition geometry with smaller incidence angles, to decrease occlusions by buildings and thus improve the results with respect to completeness.

An object based framework for building change analysis using 2D and 3D information of high resolution satellite images

With the rapid change of urban areas in developing countries, construction areas are constantly appearing in different parts of cities. Those changed areas require timely monitoring to provide up-to-date information for of urban information systems. As a result, it is a challenge to develop an effective change analysis of different objects, especially buildings in cities. This paper presents an object-based framework for analyzing building changes from high-resolution satellite stereo images (HRSSI). The disparity information extracted from stereo images and spectral information including visible vegetation index (VVI) help extract the buildings in a hierarchical approach. Evaluations show the accuracy of higher than 98% and F1-Score of higher than 87% for the building extraction step. Then, each building object is classified into three main categories including “Remained Building”, “Removed Building” or “Added Building”. Also, the “Remained Building” objects are categorized into four change states including “Only 2D Change”, “Only 3D Change”, “2D with 3D Change” and “No Change”. This is done by utilizing the object-based similarity analysis of the spectral information as well as the similarity analysis of the disparity information using CNN and their integration. Evaluations demonstrate the accuracy of higher than 97% and F1-Score of higher than 90% for this step.

EVALUATION OF SELECTED COST AGGREGATION METHODS ON HIGH RESOLUTION SATELLITE STEREO IMAGES

Abstract. Dense stereo processing requires a critical step that called cost aggregation or cost optimization. Most of the cost aggregation methods are evaluated on close range stereo images from Middlebury or KITTI datasets. While the effect of cost aggregation on high resolution satellite stereo processing has not yet been sufficiently evaluated. In this paper, three typical cost aggregation methods together with another approach which is a combination of these methods are evaluated on high resolution satellite stereo images and then are compared with LiDAR ground truth. These methods including Semi-Global Matching (SGM), Guided Filtering (GF), iterative GF (IGF), and SGM followed by GF (SGM-GF) with Census and Zero Normalized Cross Correlation (ZNCC) cost functions. Although the Census cost function has a good performance on the border of the objects and low blurring effects, the results of both cost functions, i.e. Census and ZNCC, have same treatment on all stereo methods. Also, in order to make an impartial assessment, for all stereo methods, we do not perform any disparity map refinement. The bad-pixel criteria with an absolute difference height error greater than 2 meters for SGM, GF, IGF, and SGM-GF methods is 36.7%, 34.8%, 33.8%, and 28.6% respectively. Also, the Normalized Median Absolute Difference (NMAD) error for SGM, GF, IGF, and SGM-GF is 1.29, 1.15, 1.06, and 0.94 meters, respectively. Overall, the experimental results on WV III stereo images demonstrate that the SGM method has lower accuracy and SGM-GF method is accurate than other methods.

2D/3D information fusion for building extraction from high-resolution satellite stereo images using kernel graph cuts

ABSTRACTThis study presents a building extraction strategy from High-resolution satellite stereo images (HRSSI) using 2D and 3D information fusion. In the 2D processing strategy, a visible vegetation index (VVI) is generated. In the 3D processing, a disparity map is generated using semi-global matching (SGM). To remove defects from the disparity map, an object-based approach is proposed by using mean-shift image segmentation and extracting rectangles. By removing terrain effects, a normalized disparity map (nDM) is produced. In the next step, vegetation pixels are removed from nDM and an initial building mask is generated. As nDM does not have precise building boundaries, hybrid segmentation by the kernel graph cut (KGC) is applied to the feature space including the RGB, nDM, and VVI and the results are used in a decision level fusion step. By this methodology, segments that are highly intersected with initial building mask are classified as buildings. Finally, a building boundary refinement (BBR) algorithm is applied to buildings for removing the remaining defects. The proposed method is applied to two pairs of GeoEye-1 stereo images including residential and industrial test areas. Evaluation results show the completeness and correctness level of higher than 90% for the two test areas. Further evaluations show that the quality metric has significantly changed after decision level fusion using the KGC.

Using High Resolution Stereo Imagery for Contouring: Complex Terrain Contour Mapping of Naukandi, Balochistan

Topographic maps are large scale representation of earth features. Most important characteristic of these maps is third dimension representation of objects by using contour lines. Topographic maps are the base layers of any spatial data infrastructure. Advances in spatial sciences demand more accurate and up to date topographic representation. While traditional methods of topographic surveying are time consuming and impractical in complex topographic regions. Remote sensing and GIS techniques are being used to develop topographic data of such remote areas with great accuracy, less labor and time consumption. This study uses high resolution satellite stereo images to generate Digital Elevation Model (DEM) at 1 m contour interval of complex topographic region of Baluchistan, Pakistan. Stereo images are two satellite images of same position of the earth captured along the same track with different angles. For handling and processing the stereo images Ground Control Points (GCPs) are used. GCP is a permanent referencing points needs to orient the stereo images such that they are in the same orientation as the camera was at the time of exposure. Overlapping the stereo pair according to the well distributed ground control points generates the 3D surface which is generated from 2D stereo images. This 3D surface is being compared and validated with 90 m STRM’s and 30 m ASTER datasets of the same area. The pattern of contours at an interval of 1 m is being analyzed at all three sources of DEM and most accurate pattern is being identifies through this research.

Quality assessment of digital surface models extracted from WorldView-2 and WorldView-3 stereo pairs over different land covers

Digital surface models (DSMs) extracted from very high resolution (VHR) satellite stereo images are becoming more and more important in a wide range of geoscience applications. The number of software packages available for generating DSMs has been increasing rapidly. The main goal of this work is to explore the capabilities of VHR satellite stereo pairs for DSMs generation over different land-cover objects such as agricultural plastic greenhouses, bare soil and urban areas by using two software packages: (i) OrthoEngine (PCI), based on a hierarchical subpixel mean normalized cross correlation matching method, and (ii) RPC Stereo Processor (RSP), with a modified hierarchical semi-global matching method. Two VHR satellite stereo pairs from WorldView-2 (WV2) and WorldView-3 (WV3) were used to extract the DSMs. A quality assessment on these DSMs on both vertical accuracy and completeness was carried out by considering the following factors: (i) type of sensor (i.e., WV2 or WV3), (ii) software package (i.e., PCI or RSP) and (iii) type of land-cover objects (plastic greenhouses, bare soil and urban areas). A highly accurate light detection and ranging (LiDAR) derived DSM was used as the ground truth for validation. By comparing both software packages, we concluded that regarding DSM completeness, RSP produced significantly (p < 0.05) better scores than PCI for all the sensors and type of land-cover objects. The percentage improvement in completeness by using RSP instead of PCI was approximately 2%, 18% and 26% for bare soil, greenhouses and urban areas respectively. Concerning the vertical accuracy in root mean square error (RMSE), the only factor clearly significant (p < 0.05) was the land cover. Overall, WV3 DSM showed slightly better (not significant) vertical accuracy values than WV2. Finally, both software packages achieved similar vertical accuracy for the different land-cover objects and tested sensors.

Stereo Image Retrieval Using Height and Planar Visual Word Pairs

The wide availability of high-resolution satellite stereo images has created a surging demand for effective stereo image retrieval methods. Recently, few retrieval methods have been designed specifically for stereo images having unique characteristics (e.g., viewing number and viewing angles), and often have insufficient retrieval accuracy. A new content-based stereo image retrieval method is achieved with height and planar visual word pairs, which are generated from the stereo extracted digital surface models and orthoimages. Experimental results of the International Society for Photogrammetry and Remote Sensing stereo benchmark test data set show that our method outperforms the state-of-the-art methods in terms of accuracy and stability. Our method achieves a high retrieval precision of 0.9, and has a high efficiency. Our method is stable for two stereo pairs, covering the same scene from different sensors, which usually have a small ranking difference in the returned ranking list. Our method is helpful to quickly and accurately locate desired stereo images from large quantities of multisensor stereo images.

THE EFFECT OF SHADOW AREA ON SGM ALGORITHM AND DISPARITY MAP REFINEMENT FROM HIGH RESOLUTION SATELLITE STEREO IMAGES

Abstract. Semi Global Matching (SGM) algorithm is known as a high performance and reliable stereo matching algorithm in photogrammetry community. However, there are some challenges using this algorithm especially for high resolution satellite stereo images over urban areas and images with shadow areas. As it can be seen, unfortunately the SGM algorithm computes highly noisy disparity values for shadow areas around the tall neighborhood buildings due to mismatching in these lower entropy areas. In this paper, a new method is developed to refine the disparity map in shadow areas. The method is based on the integration of potential of panchromatic and multispectral image data to detect shadow areas in object level. In addition, a RANSAC plane fitting and morphological filtering are employed to refine the disparity map. The results on a stereo pair of GeoEye-1 captured over Qom city in Iran, shows a significant increase in the rate of matched pixels compared to standard SGM algorithm.

Optimal Weight Design Approach for the Geometrically-Constrained Matching of Satellite Stereo Images

This study presents an optimal weighting approach for combined image matching of high-resolution satellite stereo images (HRSI). When the rational polynomial coefficients (RPCs) for a pair of stereo images are available, some geometric constraints can be combined in image matching equations. Combining least squares image matching (LSM) equations with geometric constraints equations necessitates determining the appropriate weights for different types of observations. The common terms between the two sets of equations are the image coordinates of the corresponding points in the search image. Considering the fact that the RPCs of a stereo pair are produced in compliance with the coplanarity condition, geometric constraints are expected to play an important role in the image matching process. In this study, in order to control the impacts of the imposed constraint, optimal weights for observations were assigned through equalizing their average redundancy numbers. For a detailed assessment of the proposed approach, a pair of CARTOSAT-1 sub-images, along with their precise RPCs, were used. On top of obtaining different matching results, the dimension of the error ellipses of the intersection points in the object space were compared. It was shown through analysis that the geometric mean of the semi-minor and semi-major axis by our method was reduced 0.17 times relative to the unit weighting approach.

Assessment of DEM derived from very high-resolution stereo satellite imagery for geomorphometric analysis

ABSTRACTVery high-resolution satellite stereo images play an important role in cartographical and geomorphological applications, provided that all the processing steps follow strict procedures and the result of each step is carefully assessed. We outline a general process for assessing a reliable analysis of terrain morphometry starting from a GeoEye-1 stereo-pair acquired on an area with different morphological features. The key steps were critically analyzed to evaluate the uncertainty of the results. A number of maps of morphometric features were extracted from the digital elevation models in order to characterize a landslide; on the basis of the contour line and feature maps, we were able to accurately delimit the boundaries of the various landslide bodies.

Automatic cloud detection for high resolution satellite stereo images and its application in terrain extraction

The automatic extraction of terrain from high-resolution satellite optical images is very difficult under cloudy conditions. Therefore, accurate cloud detection is necessary to fully use the cloud-free parts of images for terrain extraction. This paper addresses automated cloud detection by introducing an image matching based method under a stereo vision framework, and the optimization usage of non-cloudy areas in stereo matching and the generation of digital surface models (DSMs). Given that clouds are often separated from the terrain surface, cloudy areas are extracted by integrating dense matching DSM, worldwide digital elevation model (DEM) (i.e., shuttle radar topography mission (SRTM)) and gray information from the images. This process consists of the following steps: an image based DSM is firstly generated through a multiple primitive multi-image matcher. Once it is aligned with the reference DEM based on common features, places with significant height differences between the DSM and the DEM will suggest the potential cloud covers. Detecting cloud at these places in the images then enables precise cloud delineation. In the final step, elevations of the reference DEM within the cloud covers are assigned to the corresponding region of the DSM to generate a cloud-free DEM. The proposed approach is evaluated with the panchromatic images of the Tianhui satellite and has been successfully used in its daily operation. The cloud detection accuracy for images without snow is as high as 95%. Experimental results demonstrate that the proposed method can significantly improve the usage of the cloudy panchromatic satellite images for terrain extraction.

GROUND DEFORMATION EXTRACTION USING VISIBLE IMAGES AND LIDAR DATA IN MINING AREA

Abstract. Recognition and extraction of mining ground deformation can help us understand the deformation process and space distribution, and estimate the deformation laws and trends. This study focuses on the application of ground deformation detection and extraction combining with high resolution visible stereo imagery, LiDAR observation point cloud data and historical data. The DEM in large mining area is generated using high-resolution satellite stereo images, and ground deformation is obtained through time series analysis combined with historical DEM data. Ground deformation caused by mining activities are detected and analyzed to explain the link between the regional ground deformation and local deformation. A district of covering 200 km2 around the West Open Pit Mine in Fushun of Liaoning province, a city located in the Northeast China is chosen as the test area for example. Regional and local ground deformation from 2010 to 2015 time series are detected and extracted with DEMs derived from ZY-3 images and LiDAR point DEMs in the case study. Results show that the mean regional deformation is 7.1 m of rising elevation with RMS 9.6 m. Deformation of rising elevation and deformation of declining elevation couple together in local area. The area of higher elevation variation is 16.3 km2 and the mean rising value is 35.8 m with RMS 15.7 m, while the deformation area of lower elevation variation is 6.8 km2 and the mean declining value is 17.6 m with RMS 9.3 m. Moreover, local large deformation and regional slow deformation couple together, the deformation in local mining activities has expanded to the surrounding area, a large ground fracture with declining elevation has been detected and extracted in the south of West Open Pit Mine, the mean declining elevation of which is 23.1 m and covering about 2.3 km2 till 2015. The results in this paper are preliminary currently; we are making efforts to improve more precision results with invariant ground control data for validation.