An effective methodology for Bohai Sea ice detection based on gray level co-occurrence matrix (GLCM) texture analysis is proposed using MODIS 250m imagery. The method determines texture measures for ...sea ice extraction by analyzing the discrepancy of textural features between sea ice and sea water. Sea ice extent and outer edge are recognized accurately by texture segmentation owing to significant differences in texture statistical features between ice and water. The texture analysis method can properly eliminate perturbations on sea ice extraction due to suspended sediment. It effectively solves the problem of spectral confusion and sea ice misassignment in the conventional gray-threshold segmentation and ratio-threshold segmentation methods. The method eliminates the need for threshold range setting for sea ice segmentation. Taking the Bohai Sea as an example, the results of the proposed method are validated using co-temporal HJ1B-CCD 30m imagery by visual interpretation, and the accuracy of the method are evaluated using confusion matrix. The results show that the proposed method is superior and more reliable for sea ice detection compared to conventional methods, providing an ideal tool for precise sea ice extraction.
Hyperspectral anomaly detection methods based on representation model have attracted much attention in recent years. In the method, a background dictionary is used to represent each pixel linearly, ...and the residual is taken as the abnormal level of the pixel. Therefore, building a functional model with strong representation ability and a matching background dictionary is an important factor for success of the method. In the existing methods, the lack of feature utilization, the contamination of background dictionary and the dependence on prior knowledge lead to the instability of anomaly detection results, which are difficult to be applied in practice. To address the issues, this paper proposed a novel hyperspectral anomaly detection method which consists of two interconnected components: a new anomaly detection function model through the combination of LRR and CR, and the supporting background dictionary which does not need prior knowledge. The new anomaly detection model decomposed a two-dimensional normalized hyperspectral image into background and anomaly components, and reconstructed the background through a background dictionary and the corresponding coefficient matrix. The representation coefficient matrix was constrained by global low-rank and local collaborative attributes which helps background modeling. A distance weight matrix was also included to enhance the dense representation of background dictionary atoms which are similar to the testing pixel. The anomaly part was constrained by column sparsity simultaneously due to the global sparsity of anomalous targets in hyperspectral images. To further clarify the physical meaning of the model, another version that imposes non-negative and sum-to-one constraints on coefficient matrix was also proposed. The supporting background dictionary was designed for practical and reliable purposes and was implemented by dual mean shift clustering which automatically estimates parameter without prior-knowledge. The experimental results show that the proposed method can effectively improve the result of anomaly detection, and has the best ROC, AUC, and separation degree between background and anomalies for four real datasets. The average AUC of four datasets of the proposed algorithm is 2.6% higher than the method based on CR and 3.2% higher than the method based on LRR. Moreover, the proposed algorithm has stability and reliability under complex background, as well as the application on a larger hyperspectral scene shows that it offers great potential in practical use.
The on-orbit calibration of geometric parameters is a key step in improving the location accuracy of satellite images without using Ground Control Points (GCPs). Most methods of on-orbit calibration ...are based on the self-calibration using additional parameters. When using additional parameters, different number of additional parameters may lead to different results. The triangulation bundle adjustment is another way to calibrate the geometric parameters of camera, which can describe the changes in each geometric parameter. When triangulation bundle adjustment method is applied to calibrate geometric parameters, a prerequisite is that the strip model can avoid systematic deformation caused by the rate of attitude changes. Concerning the stereo camera, the influence of the intersection angle should be considered during calibration. The Equivalent Frame Photo (EFP) bundle adjustment based on the Line-Matrix CCD (LMCCD) image can solve the systematic distortion of the strip model, and obtain high accuracy location without using GCPs. In this paper, the triangulation bundle adjustment is used to calibrate the geometric parameters of TH-1 satellite cameras based on LMCCD image. During the bundle adjustment, the three-line array cameras are reconstructed by adopting the principle of inverse triangulation. Finally, the geometric accuracy is validated before and after on-orbit calibration using 5 testing fields. After on-orbit calibration, the 3D geometric accuracy is improved to 11.8m from 170m. The results show that the location accuracy of TH-1 without using GCPs is significantly improved using the on-orbit calibration of the geometric parameters.
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