Key Laboratory of Disaster Reduction and Emergency Response Engineering

Beijing, China

Key Laboratory of Disaster Reduction and Emergency Response Engineering

Beijing, China

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Zhao L.,Satellite Surveying and Mapping Application Center | Wang W.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Liu F.,Chinese Academy of Surveying and Mapping | Xia X.,Information Institute of Photogrammetry and Remote Sensing of Yunnan Province | And 2 more authors.
2011 International Symposium on Image and Data Fusion, ISIDF 2011 | Year: 2011

In this paper, the specifications of WorldView-1 and its payloads are introduced, and seven polynomial adjustment models used to improve the vendor-provided Rational Function Model (RFM) accuracy are discussed in detail. WorldView-1 images of mountainous area of Yunnan Province are used to test the correction accuracy of these adjustment models. Results show that the accuracy of seven modes is similar when using well-distributed GCPs with high accuracy and the planimetry RMS errors are better than 1.6 pixels (0.9 meter). The correction accuracy of different polynomial RFM adjustment models using sparse, badly-distributed GCPs is analyzed in this paper. Results show that with the degradation of GCPs accuracy, the accuracy of independent check points(ICPs) using zero order polynomial adjustment model remains stable, while that of one order and two order polynomial adjustment models degrade distinctly. Experiments show that zero-order polynomial RFM adjustment is the simplest, most adaptive and has highest accuracy for WorldView-1 image, and it is recommended to be used in difficult area mapping with sparse or badly-distributed GCPs. © 2011 IEEE.


Wang W.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Zhao L.,Satellite Surveying and Mapping Application Center
2011 International Symposium on Image and Data Fusion, ISIDF 2011 | Year: 2011

In this paper, 5 schemes of compensation models for Rational Function Model (RFM) is proposed to improve stereo geolocation accuracy of GeoEye-1. And experimentation is carried out using stereo pair of Hobart, Australia. The results show that the accuracy of 5 schemes is similar, and with high accurate and well-distributed Ground Control Points (GCPs) the planimetry and elevation accuracy of GeoEye-1 stereo imagery pairs are both better than 0.5-meter and meet the specification for 1:5,000 topographic map of China. © 2011 IEEE.


Wen Q.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Li L.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Liu Q.,Beihang University | Fan W.,Satellite Surveying and Mapping Application Center | And 2 more authors.
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | Year: 2013

Object detection and extraction are very important research topic in remote sensing processing and analysis. An object-oriented based accurate object extraction method was proposed by combining saliency detection and image segmentation. Firstly, a new saliency detection method which is adequate for high resolution remote sensing image analysis is presented by fusing graph-based visual saliency detection and line density visual saliency detection. By introducing line density, the proposed method can detect building regions under very complex background remote sensing images in an unsupervised manner. Then, graph-cut based segmentation is used to obtain image regions. Pixels in each region have similar saliency scores and features. Accurate boundaries of objects can be extracted by analyzing saliency of these regions. Compared with pixel based salient objects detection methods, our method has high true detection rate as well as low false detection rate by using object-oriented idea. Experimental results also demonstrate that our method can detect human buildings accurate target boundary.


Lin Y.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Fan Y.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Jiang C.,CAS Institute of Electronics | Wang Z.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Shao W.,Zhejiang Ocean University
International Journal of Antennas and Propagation | Year: 2015

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is a promising technology in radar imaging which provides a better balance of azimuth resolution and swath width compared with traditional single-input single-output (SISO) SAR. It has the potential to help scientists and engineers to design ambitious SAR system with higher resolution and wider swath. This paper studies the principle of MIMO SAR using orthogonal coding waveform and then provides the performance analysis in resolution and swath width. By using orthogonal coding waveform, lower channel interference is obtained, which makes MIMO SAR achieve wider unambiguous range swath and lower azimuth ambiguity. Simulations are carried out by means of the system parameters of real spaceborne SAR platform. A ground-based MIMO SAR imaging system with up and down chirp modulation is also designed. The performances of MIMO SAR and SISO SAR are compared, and the validity and advantage of MIMO SAR are verified. © 2015 Yueguan Lin et al.


Ye J.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Ye J.,Fujian Normal University | Ye J.,Center for Monitoring Research | Ye J.,Space Star Technology | And 5 more authors.
Intelligent Systems and Decision Making for Risk Analysis and Crisis Response - Proceedings of the 4th International Conference on Risk Analysis and Crisis Response, RACR 2013 | Year: 2013

Due to the special geographical location and topography features, the flood disasters of the coastal cities in southeast China often share the common characteristics of flood, waterlogging and tide meeting together. The paper takes Putian city as an example, analyzes the reasons, and proposes measures to reduce the risk of flood disasters, which will provide a scientific basis for urban flood control and mitigation. © 2013 Taylor & Francis Group.


Wen Q.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Xia L.,CAS Institute of Remote Sensing | Li L.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Wu W.,Key Laboratory of Disaster Reduction and Emergency Response Engineering
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | Year: 2013

The automation level of classification for remote sensing image need to be improved to satisfy the timeliness and high-precision requirements in disaster emergency monitoring and assessment. But, the artificial selection of typical samples restricts the automatic interpretation of disaster information, a problem particularly acute for the development of business operation systems. This paper implements a totally automatic object-oriented land cover classification system based on automatic sample selection. First, the candidate object samples are acquired by fuzzy clustering. Second, image features and land type features are extracted from imagery and prior knowledge, respectively. Afterward, samples can be selected by applying preset thresholds on these features. Distance metric learning is then used not only for further sample selection, but also for more accurate supervised classification. Zhouqu Debris flow disaster images are computed by this method. Results show that the classification outcomes with samples selected automatically are very close to those samples selected by hand. Our results are more stable and objective than those produced manually. Moreover, it is more convenient to batch process images automatically.


Zhang W.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Wu W.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Cui Y.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Wang W.,Key Laboratory of Disaster Reduction and Emergency Response Engineering
IOP Conference Series: Earth and Environmental Science | Year: 2014

Small satellite constellation of environment and disaster's monitoring and predicting (shorted for HJ-1) is not a mapping satellite, and its parameters of attitude and orbit cannot satisfy the requirement of geometric correction using strict imaging model. On the other hand, due to the 12000 CCD detectors and large overlay of multispectral payload named CCD carried by HJ-1 satellite, the error caused by CCD distortion cannot be ignored. Aiming at these problems of HJ-1, this paper proposes a strict orbit model algorithm based on Ground Control Point (GCP) and collinear condition equations. Through the robust estimation of parameters, this algorithm can effectively set up imaging geometric model of CCD, and satisfy the requirement of high precision geometric correction.


Li C.,Central China Normal University | Li C.,Key Laboratory of Disaster Reduction and Emergency Response Engineering | Shi W.,Hong Kong Polytechnic University | Shi W.,Wuhan University
IEEE Geoscience and Remote Sensing Letters | Year: 2014

Imagery registration and rectification is a process of transforming different sets of data into one coordinate system. A new model, i.e., the generalized-line-based iterative transformation model (GLBITM), is proposed by integrating the line-based transformation model (LBTM) and generalized point photogrammetry (GPP). First, the initial value of an affine transformation is acquired by LBTM. Then, on the basis of ground control lines (GCLs), not ground control points, the linear feature adjustment model with GPP is extended to a quadratic polynomial model and utilized to iteratively solve transformation coefficients. This process eliminates the translation amount and recalculates the scale and rotation coefficients. The authors suggest an iterative method with variable weights that is based on posterior variance estimation to improve quality control. A significant characteristic of the GLBITM is that the two endpoints of the corresponding GCLs are not necessarily conjugate points. The GLBITM integrates the advantages of the LBTM and GPP and avoids their respective shortfalls. Finally, this experiment verifies that the GLBITM gives correct, robust, and effective results that can be applied in high-resolution satellite imagery processing of multiple sensors, angles, and resolutions. © 2004-2012 IEEE.

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