Based on linear spectral mixture model (LSMM) unmixing remote sensing image

2011 ◽  
Author(s):  
Jiaodi Liu ◽  
Weibin Cao
Keyword(s):  
Remote Sensing ◽  
Mixture Model ◽  
Remote Sensing Image ◽  
Spectral Mixture

scholarly journals Remote Sensing Image Registration Using Multiple Image Features

2017 ◽  
Author(s):  
Kun Yang ◽  
Anning Pan ◽  
Yang Yang ◽  
Su Zhang ◽  
Sim Heng Ong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Mixture Model ◽  
Damage Assessment ◽  
Remote Sensing Image ◽  
Image Features ◽  
Google Earth ◽  
Geometric Distortion ◽  
Remote Sensing Images ◽  
Registration Method

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

scholarly journals Flexible Hierarchical Gaussian Mixture Model for High-Resolution Remote Sensing Image Segmentation

2020 ◽  
Vol 12 (7) ◽  
pp. 1219 ◽  
Author(s):  
Xue Shi ◽  
Yu Li ◽  
Quanhua Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Remote Sensing Image ◽  
Gaussian Mixture ◽  
Weighted Sums ◽  
Bayesian Theorem ◽  
Model Complex

The Gaussian mixture model (GMM) plays an important role in image segmentation, but the difficulty of GMM for modeling asymmetric, heavy-tailed, or multimodal distributions of pixel intensities significantly limits its application. One effective way to improve the segmentation accuracy is to accurately model the statistical distributions of pixel intensities. In this study, an innovative high-resolution remote sensing image segmentation algorithm is proposed based on a flexible hierarchical GMM (HGMM). The components are first defined by the weighted sums of elements, in order to accurately model the complicated distributions of pixel intensities in object regions. The elements of components are defined by Gaussian distributions to model the distributions of pixel intensities in local regions of the object region. Following the Bayesian theorem, the segmentation model is then built by combining the HGMM and the prior distributions of parameters. Finally, a novel birth or death Markov chain Monte Carlo (BDMCMC) is designed to simulate the segmentation model, which can automatically determine the number of elements and flexibly model complex distributions of pixel intensities. Experiments were implemented on simulated and real high-resolution remote sensing images. The results show that the proposed algorithm is able to flexibly model the complicated distributions and accurately segment images.

Application of Gaussian-Rayleigh Mixture Model in Remote Sensing Image Segmentation

2015 ◽  
Vol 35 (s1) ◽  
pp. s110004 ◽  
Author(s):  
侯一民 Hou Yimin ◽  
唐玥 Tang Yue ◽  
孙晓雪 Sun Xiaoxue ◽  
隋文秀 Sui Wenxiu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Mixture Model ◽  
Remote Sensing Image
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