Weili Jiao

A Fast and Reliable Matching Method for Automated Georeferencing of Remotely-Sensed Imagery

Due to the limited accuracy of exterior orientation parameters, ground control points (GCPs) are commonly required to correct the geometric biases of remotely-sensed (RS) images. This paper focuses on an automatic matching technique for the specific task of georeferencing RS images and presents a technical frame to match large RS images efficiently using the prior geometric information of the images. In addition, a novel matching approach using online aerial images, e.g., Google satellite images, Bing aerial maps, etc., is introduced based on the technical frame. Experimental results show that the proposed method can collect a sufficient number of well-distributed and reliable GCPs in tens of seconds for different kinds of large-sized RS images, whose spatial resolutions vary from 30 m to 2 m. It provides a convenient and efficient way to automatically georeference RS images, as there is no need to manually prepare reference images according to the location and spatial resolution of sensed images.

RPC Estimation via l(1)-Norm-Regularized Least Squares (L1LS)

A rational function model (RFM), which consists of 80 rational polynomial coefficients (RPCs), has been widely used to take the place of rigorous sensor models in photogrammetry and remote sensing. However, it is difficult to solve the RPCs because of the requirement for numerous observation data [ground control points (GCPs)] in a terrain-dependent case and the strong correlation between the coefficients (ill-poseness). Regularization methods are usually applied to cope with the correlations between the coefficients, but only ℓ2-norm regularization is used by the existing approaches (e.g., ridge estimation and Levenberg-Marquardt method). The ℓ2-norm regularization can make an ill-posed problem well-posed but does not reduce the requirement for observation data. This paper presents a novel approach to estimate RPCs using ℓ1-norm-regularized least squares (L1LS) , which provides stable results not only in a terrain-dependent case but also in a terrain-independent case. On one hand, by means of L1LS, the terrain-dependent RFM becomes practical as reliable RPCs can be obtained by using much less than 40 or 39 (if the first denominators are equal to 1) GCPs, without knowing the orientation parameters of the sensor. On the other hand, the proposed method can be applied to directly refine the terrain-independent RPCs with additional GCPs: when a single or several GCPs are used, direct refinement performs similarly to bias compensation in image space; when more GCPs are available, the direct refinement can achieve comparable accuracy of the rigorous sensor model (better than conventional bias compensation in image space) .

Automatic Line Segment Registration Using Gaussian Mixture Model and Expectation-Maximization Algorithm

Line segment registration (LSR) for image pairs is a challenging task but plays an important role in remote sensing and photogrammetry. This paper proposes a line segment registration method using Gaussian Mixture Models (GMMs) and Expectation-Maximization (EM) algorithm. Comparing to the conventional registration methods which consider the local appearance of points or line segments, the proposed method of LSR uses only the spatial relations between the line segments detected from an image pair, and it does not require the corresponding line segments sharing the same start points and end points. Although the proposed method is not confined to the transformation model between the image pair, the affine model, which is a simple and fast registration model and widely used in remote sensing, is taken to verify the proposed method. Various images including aerial images, satellite images and GIS data are used to test the algorithm, and test results show that the method is robust to different conditions, including rotation, noise and illumination. The results of the proposed method are compared with those of other line segment matching methods, and it is shown that the proposed method is superior in matching precision and performs better in less-texture or no-texture case.

Towards an operational method for land surface temperature retrieval from Landsat 8 data

ABSTRACT Land surface temperature (LST) is a key parameter in the physics of land surfaces through the processes of energy and water exchange with the atmosphere. For Landsat data with only one thermal infrared channel (Landsat 4 to Landsat 7), LST cannot actually be retrieved, and external data sources, such as meteorological observations or Moderate Resolution Imaging Spectroradiometer (MODIS) data, are needed to obtain the water vapour content parameter (an important input parameter for the LST retrieval algorithm); this results in limitations on deriving LST. However, the band designations of the Landsat 8 sensors enable the derivation of LST from the Landsat 8 data. This article demonstrates an LST retrieval methodology that makes use of only Landsat 8 image data. In this methodology, the split-window covariance-variance ratio (SWCVR) technique is introduced to derive water vapour content from Landsat 8. A comparison between the retrieved LST and the in situ LST measurements shows good accuracy, with a root mean squared error (RMSE) of 0.83 K. The fact that the proposed LST estimation method utilizing solely Landsat 8 image data does not rely on any external data is a significant advantage for the development of an operational Landsat 8 LST product generating system.

Information Extraction Based on SAM of ASTER Image

The extensive clayization of Dexing copper mine is responsible for water and soil pollutions because of the oxidation of sulfide minerals. During the clayization, harmful elements such as As, S, Sb and Pb could be effectively released into the clay and water. Using the abundant spectral information of short wave infrared (SWIR), combining with the spectral absorbed index, SAM (Spectrum Angle Mapper) which based on spectral matching, subtly compartmentalizes the degree of clay and distribution in copper mine.

Accuracy analysis of remote sensing image rectification

Position accuracy is the base of remote sensing image application. In this paper, the effect of the number, the distribution and the accuracy of ground control points (GCPs) and DEM in different scales for the image rectification is analyzed in detail. Quantitative evaluation of orthoimage is performed. The mathematical functions for calculating the position accuracy of the orthoimage are given based on different georeference information. The relation between the final accuracies of orthoimages and the accuracies of GCPs and DEM is analyzed based on the experiment results. It shows that accuracies of the checked orthoimages coincide with the calculated accuracies. The final accuracy can be estimated with the method described in this paper if the accuracy of control data is known. On the other hand, if the final accuracy of the orthoimage were determined, the least requirements for the accuracies of GCPs and DEM could be calculated by the mathematical functions.

A Novel Image Registration Method Based on Phase Correlation Using Low-Rank Matrix Factorization With Mixture of Gaussian

Image registration is a critical process for the various applications in the remote sensing community, and its accuracy greatly affects the results of the subsequent applications. Image registration based on phase correlation has been widely concerned due to its robustness to gray differences and efficiency. After calculating the normalized cross-relation matrix

Sequential pattern mining of land cover dynamics based on time-series remote sensing images

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An Integrated Method to Generate a Cloud-Free Image Automatically Based on Landsat5 Data

, the most commonly used approach is fitting the 2-D phase plane that passes through the origin, but it needs to remove contaminated spectrum carefully and the corresponding parameters are empirical. In fact, the phase correlation matrix is rank one for a noise-free translation model. This property simplifies the matching problem to finding the best rank-one approximation of the normalized cross-relation matrix. We develop a novel algorithm that performs the rank-one matrix factorization on the phase correlation matrix by assuming its noise as mixture of Gaussian (MoG) distributions. The MoG model is a general approximator for any continuous distribution, and hence is able to model a wide range of noise distribution. The parameters of the MoG model can be evaluated under the framework of maximum likelihood estimation by using an expectation-maximization method, and the subspace is calculated with standard methods. The advantages of the algorithm, high accuracy, and robustness to aliasing, noise, gray difference, and occlusions are illustrated by a series of simulated and real-image experiments.

Generation of ready to use (RTU) products over China based on Landsat series data

Remote sensing images constitute a new type of multimedia data well suited to land cover change detection tasks, as they can repetitively provide information about the land surface and its changes over large and inaccessible areas. With plans for more missions and higher resolution earth observation systems, the challenge is increasingly going to be the efficient usability of the millions of collected images, especially the decades of remote sensing image time series, to describe land cover and/or scene evolution and dynamics. In contrast to traditional land cover change measures using pair-wise comparisons that emphasize the compositional or configurational changes between dates, this research focuses on the analysis of the temporal sequence of land cover dynamics, which refers to the succession of land cover types for a given area over more than two observational periods. The expected novel significance of this study is the generalization of the application of the sequential pattern mining method for capturing the spatial variability of landscape patterns and their trajectories of change to reveal information regarding process regularities with satellite imagery. Experimental results showed that this approach not only quantifies land cover changes in terms of the percentage area affected and maps the spatial distribution of these land cover changes but also reveals possibly interesting or useful information regarding the trajectories of change. This method is a valuable complement to existing bi-temporal change detection methods.