Bankline detection of GF-3 SAR images based on shearlet

The GF-3 satellite is China’s first self-developed active imaging C-band multi-polarization synthetic aperture radar (SAR) satellite with complete intellectual property rights, which is widely used in various fields. Among them, the detection and recognition of banklines of GF-3 SAR image has very important application value for map matching, ship navigation, water environment monitoring and other fields. However, due to the coherent imaging mechanism, the GF-3 SAR image has obvious speckle, which affects the interpretation of the image seriously. Based on the excellent multi-scale, directionality and the optimal sparsity of the shearlet, a bankline detection algorithm based on shearlet is proposed. Firstly, we use non-local means filter to preprocess GF-3 SAR image, so as to reduce the interference of speckle on bankline detection. Secondly, shearlet is used to detect the bankline of the image. Finally, morphological processing is used to refine the bankline and further eliminate the false bankline caused by the speckle, so as to obtain the ideal bankline detection results. Experimental results show that the proposed method can effectively overcome the interference of speckle, and can detect the bankline information of GF-3 SAR image completely and smoothly.

A real time detection method for abnormal strapping of steel coil based on CCD active imaging

In order to accurately detect the abnormal looseness of strapping in the process of steel coil hoisting, an accurate detection method of strapping abnormality based on CCD structured light active imaging is proposed. Firstly, a maximum entropy laser stripe automatic segmentation model integrating multi-scale saliency features is constructed. With the help of saliency detection model, the purpose is to reduce the interference of the environment to the laser stripe and highlight the distinguishability between the stripe and the background. Then, the maximum entropy is used to segment the fused saliency features and accurately extract the stripe contour. Finally, the stripe normal field is obtained by calculating the stripe gradient vector, the stripe center line is extracted based on the stripe distribution normal direction, and the abnormal strapping is recognized online according to the stripe center. Experiments show that the proposed method is effective in terms of detection accuracy and time efficiency, and has certain engineering application value.

A Corner Detection Algorithm Based on Regional Center of Mass in Imaging through Water Surface

Structural light active imaging can obtain more information about the target scene, which is widely used in image registration,3D reconstruction of objects and motion detection. Due to the random fluctuation of water surface and complex underwater environment, the current corner detection algorithm has the problems of false detection and uncertainty. This paper proposes a corner detection algorithm based on the region centroid extraction. Experimental results show that, compared with the traditional detection algorithms, the proposed algorithm can extract the feature point information of the image in real time, which is of great significance to the subsequent image restoration.

Computer Image Recognition and Recovery Method for Distorted Underwater Images by Structural Light

When imaging through water surface, the random fluctuation of sea surface will cause the distortion of the target scene image, so the distorted image needs to be corrected and reconstructed. At present, distortion compensation mainly adopts iterative registration strategy based on image sequences which is difficult to satisfy the real-time observation. This paper presents a correction method based on active imaging of structured light for underwater image. Experimental results show that compared with the traditional iterative algorithm, the proposed algorithm cannot only improve the restoration accuracy, but also greatly shorten the processing time. Experimental test results demonstrate that the proposed algorithm has good recovery results.

Performance Analysis Of Multispectral Lidar In Land Cover Classification

The Optech Titan is the world’s first multispectral airborne Light Detection and Ranging (LIDAR) sensor, a revolutionary sensor that includes three active imaging channels of different wavelengths for day or night mapping of complex environments. Multispectral imagery and monochromatic LIDAR have long existed as independent technologies and both systems have developed workflows to perform land cover classification. This project was undertaken to analyze the performance of Optech Titan’s three active imaging channels and LIDAR attributes in land cover classification. By processing selective parameters through the multispectral image land cover classification process, we can determine the accuracy performance of individual channels and attributes in land cover classification. The outcome of this process will measure the effectiveness of combining LIDAR attributes with multispectral imagery for land cover classification. The test site was a 600m x 600m residential neighbourhood in Oshawa, Ontario captured at point-spacing of 0.5 meter. Multispectral imagery had an overall accuracy result of 77%. The most accurate land cover classification result from our testing was 77.5%. This was produced as a special index scenario by using the three intensities along with the nDSM. It is apparent from the results that the intensity-attribute provides the most useful information in land cover classification. The highest monochromatic LIDAR accuracy result of 70% came from Channel 2 (NIR - 1024 mm). Channel 2’s accuracy is only 7% lower than multispectral imagery result. Channel 1 and 3 had less-favorable results at 59.5% and 58% respectively. Individual land cover classification tests on Z-attribute and N-attribute produced unfavorable results of 37% and 47.5% respectively.

Performance Analysis Of Multispectral Lidar In Land Cover Classification

The Optech Titan is the world’s first multispectral airborne Light Detection and Ranging (LIDAR) sensor, a revolutionary sensor that includes three active imaging channels of different wavelengths for day or night mapping of complex environments. Multispectral imagery and monochromatic LIDAR have long existed as independent technologies and both systems have developed workflows to perform land cover classification. This project was undertaken to analyze the performance of Optech Titan’s three active imaging channels and LIDAR attributes in land cover classification. By processing selective parameters through the multispectral image land cover classification process, we can determine the accuracy performance of individual channels and attributes in land cover classification. The outcome of this process will measure the effectiveness of combining LIDAR attributes with multispectral imagery for land cover classification. The test site was a 600m x 600m residential neighbourhood in Oshawa, Ontario captured at point-spacing of 0.5 meter. Multispectral imagery had an overall accuracy result of 77%. The most accurate land cover classification result from our testing was 77.5%. This was produced as a special index scenario by using the three intensities along with the nDSM. It is apparent from the results that the intensity-attribute provides the most useful information in land cover classification. The highest monochromatic LIDAR accuracy result of 70% came from Channel 2 (NIR - 1024 mm). Channel 2’s accuracy is only 7% lower than multispectral imagery result. Channel 1 and 3 had less-favorable results at 59.5% and 58% respectively. Individual land cover classification tests on Z-attribute and N-attribute produced unfavorable results of 37% and 47.5% respectively.