Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

THE HIGH RESOLUTION SEISMIC TOMOGRAPHIC IMAGE IN QINGHAI—TIBET PLATEAU AND ITS DYNAMIC IMPLICATIONS

The Qinghai—Tibet plateau and its surrounding areas including Indian subcontinent, Xinjiang, Mongolia, is a largest lithosphere convergence place in the world, which characterized by continent\|continent collision with a thick crust and lithosphere. The high resolution seismic surface wave tomographic inversion has been conducted for studying the 3D velocity structure of crust and upper mantle in those areas. The seismic surface waveform data are from the archives of the CDSN, GSN and GEOSCOPE. About 2400 long period surface waveform recordings are available for both dispersion and waveform tomographic inversion. The block inversion by grid 1°×1°in Qinghai—Tibet plateau and 2°×2°in the surrounding areas were adapted. The resulting maps show the high resolution 3D shear wave velocity variation from earth’s surface to 400km depth.

Road Vector Map Change Monitoring Based on High Resolution Remote Sensing Images

Some studies about road vector map change detection were done in this paper. Firstly, on the basis of old road vector data, the original high resolution remote sensing image was cut into segments. Then, gray analysis and edge extraction of those segments were done so that changes of roads could be detected. Finally, according to the vector data and gray information of roads which were not changed, road templates were extracted and saved automatically. This method was performed on the World View high resolution image of certain parts in the country. The detection result shows that detection correctness is 79.56% and completeness can reach 97.72%. Moreover, the extracted road templates are essentials for the template matching method of road extraction.

Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.

Monitoring of Karst Rocky Desertification Control Projects Based on Remote Sensing Images with Medium and High Spatial Resolution——A Case Study of Disi River Basin in Puan County

[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.

High-resolution Solar Image Reconstruction Based on Non-rigid Alignment

Suppressing the interference of atmospheric turbulence and obtaining observation data with a high spatial resolution are an issue to be solved urgently for ground observations. One way to solve this problem is to perform a statistical reconstruction of short-exposure speckle images. Combining the rapidity of Shift-Add and the accuracy of speckle masking, this paper proposes a novel reconstruction algorithm-NASIR(Non-rigid Alignment based Solar Image Reconstruction). NASIR reconstructs the phase of the object image at each frequency by building a computational model between geometric distortion and intensity distribution and reconstructs the modulus of the object image on the aligned speckle images by speckle interferometry. We analyzed the performance of NASIR by using the correlation coefficient, power spectrum, and coefficient of variation of intensity profile in processing data obtained by the NVST(1 m New Vacuum Solar Telescope). The reconstruction experiments and analysis results show that the quality of images reconstructed by NASIR is close to speckle masking when the seeing is good, while NASIR has excellent robustness when the seeing condition becomes worse. Furthermore, NASIR reconstructs the entire field of view in parallel in one go, without phase recursion and block-by-block reconstruction, so its computation time is less than half that of speckle masking. Therefore, we consider NASIR is a robust and highquality fast reconstruction method that can serve as an effective tool for data filtering and quick look.

Automatic Road Extraction Using Particle Filters from High Resolution Images

Road extraction plays an important role in many applications such as car navigation, but the manual extraction of roads is a laborious, tedious task. To speed the extraction of roads, an approach based on particle filtering to extract automatically roads from high resolution imagery is proposed. Particle filtering provides a statistical framework for propagating sample-based approximations of posterior distributions and has almost no restriction on the ingredients of the model. We integrate the similarity of grey value and the edge point distribution of roads into particle filtering to deal with complex scenes. To handle road appearance changes the tracking algorithm is allowed to update the road model during temporally stable image observations. A fully automatic initialization strategy is used. Experimental results show that the proposed approach is a promising and fully automatic method for extracting roads from images, even in the presence of occlusions.

Use of high-resolution X-ray computed tomography and 3D image analysis to quantify mineral dissemination and pore space in oxide copper ore particles

Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance. To quantify the mineral dissemination and pore space distribution of an ore particle, a cylindrical copper oxide ore sample (I center dot 4.6 mm x 5.6 mm) was scanned using high-resolution X-ray computed tomography (HRXCT), a nondestructive imaging technology, at a spatial resolution of 4.85 mu m. Combined with three-dimensional (3D) image analysis techniques, the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated. In addition, the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques. Furthermore, the pore phase features, including the pore size distribution, pore surface area, pore fractal dimension, pore centerline, and the pore connectivity, were investigated quantitatively. The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated, with a large surface area and low connectivity. This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.

Smart Photogrammetric and Remote Sensing Image Processing for Very High Resolution Optical Images——Examples from the CRC-AGIP Lab at UNB

This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB), Canada. The techniques were developed by innovatively/“smartly” utilizing the characteristics of the available very high resolution optical remote sensing images to solve important problems or create new applications in photogrammetry and remote sensing. The techniques to be introduced are: automated image fusion (UNB-PanSharp), satellite image online mapping, street view technology, moving vehicle detection using single set satellite imagery, supervised image segmentation, image matching in smooth areas, and change detection using images from different viewing angles. Because of their broad application potential, some of the techniques have made a global impact, and some have demonstrated the potential for a global impact.

Retrieval of High Resolution Satellite Images Using Texture Features

In this research, a content-based image retrieval （CBIR） system for high resolution satellite images has been developed by using texture features. The proposed approach uses the local binary pattern （LBP） texture feature and a block based scheme. The query and database images are divided into equally sized blocks, from which LBP histograms are extracted. The block histograms are then compared by using the Chi-square distance. Experimental results show that the LBP representation provides a powerful tool for high resolution satellite images （HRSI） retrieval.

Monitoring of vegetation coverage based on high-resolution images

Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.

Level study on fractal characteristics of tidal creeks and information of seashell habitats in the Gaizhou Beach based on high-resolution satellite images

The fractal characteristics of tidal creeks in the Gaizhou Beach are analyzed based on high-resolution images fusionof Landsat TM and ERS2, and then the graphic models and characteristics of converse information tree of tidalcreeks in the Gaizhou Beach are established. A calculation model is established based on the above results, and at thesame time, quantitative calculation of the evolution characteristics and the diversity between the northern and thesouthern parts of the Gaizhou Beach is carried out. By the supervised classification of these images, distribution andareas of high tidal flats, middle tidal flats and low tidal flats in the Gaizhou Beach are studied quantitatively, and imagecharactistics of seashell habitats in the Gaizhou Beach and the correlation between mudflat distribution and seashellhabitats are studied. At last, the engineering problems in the Gaizhou Beach are discussed.

LAND-COVER DENSITY-BASED APPROACH TO URBAN LAND USE MAPPING USING HIGH-RESOLUTION IMAGERY

Nowadays, remote sensing imagery, especially with its high spatial resolution, has become an indispensable tool to provide timely up-gradation of urban land use and land cover information, which is a prerequisite for proper urban planning and management. The possible method described in the present paper to obtain urban land use types is based on the principle that land use can be derived from the land cover existing in a neighborhood. Here, moving window is used to represent the spatial pattern of land cover within a neighborhood and seven window sizes (61m×61m, 68m×68m, 75m×75m, 87m×87m, 99m×99m, 110m×110m and 121m×121m) are applied to determining the most proper window size. Then, the unsupervised method of ISODATA is employed to classify the layered land cover density maps obtained by the moving window. The results of accuracy evaluation show that the window size of 99m×99m is proper to infer urban land use categories and the proposed method has produced a land use map with a total accuracy of 85%.

Analysis of Urban Greening of Tai'an City in China Based on Remote Sensing Image with High Resolution

By taking urban greening of Tai'an City of Shandong Province for example,selecting remote sensing image Quickbird with high resolution,and combining visual interpretation with automatic classification of the computer,based on urban green space systematic planning map,green space information of the built-up area has been selected for the research centering on green lands in urban parks,productive green lands,green lands attached to residential areas and units,green lands attached to the road,other green lands,water surfaces and so on.Through the statistics and analysis,the distribution condition of each type of urban green land has been obtained,and some suggestions have been proposed in view of existing problems of urban greening.It should enhance the construction of green lands in urban parks,residential areas and units,improve road greening level,implement vertical greening,increase the area of productive green lands and fully make use of idle lands.

Geometric Calibration and Image Quality Assessment of High Resolution Dual-Camera Satellite

The evaluation of geometric calibration accuracy of high resolution satellite images has been increasingly recognized in recent years.In order to evaluate geometric accuracy for dual-camera satellite images based on the ground control points(GCP),a rigorous geometric imaging model,which was based on the collinear equation of the probe directional angle and the optimized tri-axial attitude determination(TRIAD)algorithm,is presented.Two reliable test fields in Tianjin and Jinan(China)were utilized for geometric accuracy validation of Pakistan Remote Sensing Satellite-1.The experimental results demonstrate a certain deviation of the on-orbit calibration result from the initial design values of the calibration parameters.Therefore,on-orbit geometric calibration is necessary for optical satellite imagery.Within this research,the geometrical performances including positioning accuracy without/with GCP and band registration of the dual-camera satellite were analyzed in detail,and the results of geometric image quality are assessed and discussed.As a result,it is feasible and necessary to establish such a geometric calibration model to evaluate the geometric quality of dual-camera satellite.

Intelligent High Resolution Satellite/Aerial Imagery

High resolution satellite images are rich source of geospatial information. Nowadays, these images contain finest spectral and spatial information of ground realities in different electromagnetic spectrum. Many image processing softwares, algorithms and techniques are available to extract such information from these images. Multi spectral as well as panchromatic (PAN) high resolution satellite images are missing, one important information, regarding ground features and realities that information is attribute information which is not directly available in high resolution satellite images. From very first day, this information used to be collected through indirect ways using GPS, digitizing, geo-coding, geo tagging, field survey and many other techniques. Our real world has vertical labels for ground observer to identify and use this information. These vertical labels are present in form of names, logos, icons, symbols and numbers. These vertical labels ease us to work in real world. Satellites are unable to read these labels due to their vertical orientation. Making satellite/aerial imagery rich of attribute information, we have the possibility to design our world accordingly. Just like vertical labels we can also place real physical horizontal label for space sensors, to make this information directly available in high resolution satellite/aerial imagery. This work is about possibilities of such techniques and methods.

A Two-Stage Algorithm of High Resolution Image Alignment for Mobile Applications

Global motion estimation (GME) algorithms are widely applied to computer vision and video processing. In the previous works, the image resolutions are usually low for the real-time requirement (e.g. video stabilization). However, in some mobile devices applications (e.g. image sequence panoramic stitching), the high resolution is necessary to obtain satisfactory quality of panoramic image. However, the computational cost will become too expensive to be suitable for the low power consumption requirement of mobile device. The full search algorithm can obtain the global minimum with extremely computational cost, while the typical fast algorithms may suffer from the local minimum problem. This paper proposed a fast algorithm to deal with 2560 × 1920 high-resolution (HR) image sequences. The proposed method estimates the motion vector by a two-level coarse-to-fine scheme which only exploits sparse reference blocks (25 blocks in this paper) in each level to determine the global motion vector, thus the computational costs are significantly decreased. In order to increase the effective search range and robustness, the predictive motion vector (PMV) technique is used in this work. By the comparisons of computational complexity, the proposed algorithm costs less addition operations than the typical Three-Step Search algorithm (TSS) for estimating the global motion of the HR images without the local minimum problem. The quantitative evaluations show that our method is comparable to the full search algorithm (FSA) which is considered to be the golden baseline.

New Mathematical Model Based on Affine ransformation for Remote Sensing Image with High Resolution

This paper calculates the parameters of image position and orientation,proposes a mathematical model and adopts a new method with three steps of transformations based on parallel ray projection.Every step of the model is strict,and the map function of each transformation is the first order polynomials and other simple function.The final calculation of the parameters is for the linear equations with good status.As a result,the problem of the relativity of image parameter calculation is solved completely.Some experiments are carried out.