In order to extract urban impervious surfaces (ISs) accurately, optical and synthetic aperture radar (SAR) images fusion was recognized as one promising method. However, most fusion methods currently focus on feature-level and decision-level fusions. There are only a few studies exploring the performance of the fused image at the pixel level for IS extraction. Therefore, we introduced the shearlet transform to fuse Landsat-8 and TerraSAR images and evaluated the fused image by comparing it to those obtained using conventional image fusion methods. The IS from the fused images using the support vector machine algorithm is extracted and compared. Experimental results indicate some interesting findings. First, the shearlet transform can fully retain the spectral information from the optical image and the spatial information from the SAR image. Second, the IS extraction from the fused image with the shearlet transform achieved the highest accuracy with an overall accuracy of 95.1% and a Kappa coefficient of 0.8792, which confirmed the proposed method is applicable to IS extraction. We can conclude that an effective pixel-level fusion algorithm for optical and SAR images can significantly improve the extraction accuracy of urban IS. Our research could provide an innovative fusion technique and also could serve as a meaningful reference for further applications of optical and SAR imagery. In addition, the potential of SAR data in IS extraction should be further investigated.
The recent advance of satellite technology has led to explosive growth of high-resolution remote sensing images in both quantity and quality. To address the challenges of high-resolution remote sensing images retrieval in both efficiency and accuracy, a distributed system architecture for satellite images retrieval by combining deep and traditional hand-crafted features is proposed in this paper. On one hand, to solve the problem of higher computational complexity and storage capacity, Hadoop framework is applied to manage satellite image data and to extract image features in parallel environment. On the other hand, deep features based on convolutional neural networks (CNNs) are extracted and combined with traditional features to overcome the limitations of hand-crafted features. Besides, object detection are integrated in the proposed system to realize accurate object locating at the time of retrieval. Experiments are carried on several challenging datasets to evaluate the performance of the proposed distributed system. Standard metrics like retrieval precision, recall and computing time under different configurations are compared and analyzed. Experimental results demonstrate that our system architecture is practical and feasible, both efficiency and accuracy can meet realistic demands.
This paper presents a point cloud optimization method of low-altitude remote sensing image based on least square matching (LSM). The proposed method is designed to be especially effective for addressing the conundrum of stereo matching on the discontinuity of architectural structures. To overcome the error matching and blur on building discontinuities in three-dimensional (3-D) reconstruction, a pair of mutually perpendicular patches is set up for every point of object discontinuities instead of a single patch. Then an error equation is built to compute the optimal point according to the LSM method, space geometry relationship, and collinear equation constraint. Compared with the traditional patch-based LSM method, the proposed method can achieve higher accuracy 3-D point cloud data and sharpen the edge. This is because a geometric mean patch in patch-based LSM is the local tangent plane of an object’s surface. Using a pair of mutually perpendicular patches instead of a single patch evades the problem that the local tangent plane on the discontinuity of a building did not exist and highlights the edges of buildings. Comparison studies and experimental results prove the high accuracy of the proposed algorithm in low-altitude remote sensing image point cloud optimization.
Accurate estimation of forest aboveground biomass (AGB) is crucial for monitoring ecosystem responses to environmental change. Optical remote sensing is the most widely used method for obtaining AGB information. However, there is a need for improving the accuracy of AGB estimation obtained in this way. A synergistic estimation model through the integration of spectral and textural features from Chinese high spatial resolution satellite data GaoFen-1 (GF-1) for AGB estimation in the arid region of Ejin Banner, Inner Mongolia Autonomous Region, China, was put forward. The proposed model combined the spectrum-alone model and texture-alone model, which were developed to describe the relationship between image parameters (spectral vegetation indices or texture parameters) obtained from GF-1 data and field measurements, and determined the contributions of spectral and textural sensitive indices to biomass estimation under different biomass conditions. The synergistic model was verified by comparison with the ground measurements and the results of the spectrum-alone and texture-alone models. The results indicate that the proposed synergistic estimation model is more effective than the spectrum-alone or texture-alone model, and shows considerable potential in forest AGB estimation by combining spectral and textural information.
In this paper, a progressive texture retrieval algorithm for remotely sensed imagery based on Contourlet spectral
histogram is proposed. Contourlet transform is applied to extract texture features of remotely sensed imagery from
different scales and different directions. Decomposed low-pass subband and high-pass subbands are used to realize
coarse and fine retrieval respectively. The proposed algorithm not only utilizes the advantages of Contourlet on multiscale
and multi-direction feature representation and extraction, but also utilizes the efficiency of spectral histogram on
distributed statistical feature description. Experimental results prove that Contourlet Spectral Histogram provides a
powerful tool for texture retrieval of remotely sensed imagery.
In this paper, the technical attribute developing from the fixed wireless broadband technology (IEEE802.16-2004) to
mobile wireless broadband technology (IEEE802.16e-2005) is discussed. A mobile WiMAX (Worldwide Interoperability for Microwave Access) network structure is designed for the special needs of mobile digital city. This paper designed and optimized the specific network structure. The function of mobile wireless video, audio, data service
and others, which can manage and service for the mobile digital city are realized based on the mobile WiMAX network.
Nowadays increasing attention has been paid to reasonable organization and effective management of vast amounts of
remotely sensed data for the goal of quick browse, convenient query and Retrieval-on-Demand service.
In this paper, in order to reach compromise among precision, efficiency and storage and to realize ROI coding, data
partition based on Nona-tree data structure and data compression based on JPEG2000 are adopted to organize and
manage original remotely sensed images. Afterwards, a prototype system in three-tier B/S mode is developed to test the
validity of our data organization and management strategy for content-based retrieval mentioned above. In this system,
texture-based and shape-based feature extraction algorithms based on wavelet transformation, math morphology and
other relative theory are applied. Corresponding feature descriptor and similarity calculation are also given. At last,
experimental results are given to show that the strategy proposed in this paper is valid, followed by brief conclusions and
future directions. The work of this paper is useful to push the development of geo-spatial information services and
promote content-based retrieval of remotely sensed images from experimentation to practicality.
How to recognize man-made objects from high-resolution remote sensing images has been considered an attractive and important research field in remote sensing applications undoubtedly. In this paper we try to present a feasible contour-based retrieval strategy of remote sensing images. The merit of our strategy is it can avoid the impact caused by the difficult of automatic manmade object discrimination so far and the deficiency of huge computational volume aroused by template matching. Besides, on the basis of analyzing the limitations of common descriptors such as Fourier descriptor and Hu invariant moments, invariant relative moments are adopted to describe shape feature of man-made objects in our retrieval strategy. After describing contour feature extraction method, feature matching method and retrieval process based on shape feature, a prototype system is also designed and implemented to prove the validity and accuracy of our strategy mentioned above. In our experiments three types of man-made objects with different shape feature, i.e., boat, oilcan and buildings with flat-roof, are selected as our research targets. Experimental results illustrate that our strategy is feasible and the corresponding retrieval performance is analyzed, followed by conclusions and future works.
KEYWORDS: 3D modeling, Buildings, 3D image processing, Data modeling, Reconstruction algorithms, 3D image reconstruction, Data acquisition, Lithium, Laser scanners, Image fusion
In this paper, a topology-based strategy for 3D reconstruction of complicated buildings from stereo image pair is put forward. It comes from our investigation on the applicability of topology analysis and a strongly topology-driven process that combines different levels of geometrical description with different levels of topological abstraction.
The authors emphasize the topology-based strategy on different levels of geometrical description: Firstly a topology-based 3D data model is presented in which the topological relationships within a building or between geometrical objects are described implicitly or explicitly. Secondly based on description of vertexes level, interested vertexes are collected from stereo image pair and saturated attribute of each interior vertex is defined, furthermore an adjacency table is defined to store the connection attributes of verges automatically. Thirdly surfaces are looked on as polygons with closed verges on the basis of bi-directional querying of the adjacency table. Finally complicated buildings are described as graphs with interior and exterior topological attributes. Based on the strategy mentioned above, a software platform for 3D reconstruction of complicated buildings is built up. The efficiency of suggested method is examined through practical experiments.
The 3D model of interchanges is one of the fundamental components of the city models and has got researcher’s extensive concern in recent years. However, solution to automatic extraction of 3D man-made complicated objects is still unavailable up to now because automatic interpretation of spatial image lacks required performance for practical applications. In this paper, an integrated method involving stereo image pair, CAD, DPW and VR technology for 3D reconstruction of the interchange is put forward and various solutions are presented to meet the demands of the Cyber City according to application requirements. Besides, the semantics of interchange as a whole is used to control and to evaluate the quality of interchange model extraction in all the reconstruction process. Finally, a software platform for 3D reconstruction of the interchange using OpenGL and VC++ is built up and the efficiency of suggested method is examined through practical case studies.
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