There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge ...There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge detection method for remote sensing image based on variable structuring element is proposed. Firstly, the structuring elements with different scales and multiple directions are constructed according to the diversity of remote sensing imagery targets. In order to suppress the noise of the target background and highlight the edge of the image target in the remote sensing image by adaptive Top hat and Bottom hat transform, the corresponding adaptive morphological operations are constructed based on variable structuring elements; Secondly, adaptive morphological edge detection is used to obtain multiple images with different scales and directional edge features; Finally, the image edges are obtained by weighted summation of each direction edge, and then the least square is used to fit the edges for accurate location of the edge contour of the target. The experimental results show that the proposed method not only can detect the complete edge of remote sensing image, but also has high edge detection accuracy and superior anti-noise performance. Compared with classical edge detection and the morphological edge detection with a fixed single structuring element, the proposed method performs better in edge detection effect, and the accuracy of detection can reach 95 %展开更多
[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IR...[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.展开更多
基金National Natural Science Foundation of China(No.61761027)Postgraduate Education Reform Project of Lanzhou Jiaotong University(No.1600120101)
文摘There are problems such as incomplete edges and poor noise suppression when a single fixed morphological structuring element is used to detect the edges in remote sensing images. For this reason, a morphological edge detection method for remote sensing image based on variable structuring element is proposed. Firstly, the structuring elements with different scales and multiple directions are constructed according to the diversity of remote sensing imagery targets. In order to suppress the noise of the target background and highlight the edge of the image target in the remote sensing image by adaptive Top hat and Bottom hat transform, the corresponding adaptive morphological operations are constructed based on variable structuring elements; Secondly, adaptive morphological edge detection is used to obtain multiple images with different scales and directional edge features; Finally, the image edges are obtained by weighted summation of each direction edge, and then the least square is used to fit the edges for accurate location of the edge contour of the target. The experimental results show that the proposed method not only can detect the complete edge of remote sensing image, but also has high edge detection accuracy and superior anti-noise performance. Compared with classical edge detection and the morphological edge detection with a fixed single structuring element, the proposed method performs better in edge detection effect, and the accuracy of detection can reach 95 %
基金Supported by Science and Technology Project of Lianyungang City(SH0917)
文摘[Objective] The aim was to extract red tide information in Haizhou Bay on the basis of multi-source remote sensing data.[Method] Red tide in Haizhou Bay was studied based on multi-source remote sensing data,such as IRS-P6 data on October 8,2005,Landsat 5-TM data on May 20,2006,MODIS 1B data on October 6,2006 and HY-1B second-grade data on April 22,2009,which were firstly preprocessed through geometric correction,atmospheric correction,image resizing and so on.At the same time,the synchronous environment monitoring data of red tide water were acquired.Then,band ratio method,chlorophyll-a concentration method and secondary filtering method were adopted to extract red tide information.[Result] On October 8,2005,the area of red tide was about 20.0 km2 in Haizhou Bay.There was no red tide in Haizhou bay on May 20,2006.On October 6,2006,large areas of red tide occurred in Haizhou bay,with area of 436.5 km2.On April 22,2009,red tide scattered in Haizhou bay,and its area was about 10.8 km2.[Conclusion] The research would provide technical ideas for the environmental monitoring department of Lianyungang to implement red tide forecast and warning effectively.
文摘在现有基于物理模型的高光谱异常探测HAD(Hyperspectral Anomaly Detection)方法中,低秩表示LRR(Low-Rank Representation)模型以其强大的背景和目标特征分离能力而受到广泛的关注和采用。然而,由于依赖手动参数的选择以及较差的泛化性,导致其实际应用受到限制。为此,本文将LRR模型与深度学习技术相结合,提出了一种新的适用于HAD的基础深度展开网络,称为LRR-Net。该方法借助交替方向乘法ADMM(Alternating Direction Method of Multipliers)优化器高效地求解LRR模型,并将其求解步骤耦合至深度网络中以指导其搜索过程,为深度网络提供了一定的理论基础,具有较强的可解释性。此外,LRR-Net以端到端的方式将一系列正则化的参数转换为可学习的网络参数,从而避免了手动调参。4组不同的高光谱异常探测实验证明了LRR-Net的有效性,与其他无监督的异常探测方法相比,LRR-Net具有较强的泛化性和鲁棒性,能够提高HAD的精度。
