scholarly journals Extract enclosure culture in lakes based on remote sensing image texture information

2006 ◽  
Vol 18 (4) ◽  
pp. 337-342 ◽  
Author(s):  
LI Junjie ◽  
◽  
HE Longhua ◽  
DAI Jingfang ◽  
LI Jinlian
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Texture ◽  
Texture Information

scholarly journals TEXTURE CLUSTERING OF SATELLITE IMAGES USING SELF-ORGANIZING NEURAL NETWORK

2014 ◽  
pp. 15-21
Author(s):  
M. M. Lukashevich ◽  
R. Kh. Sadykhov
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Satellite Images ◽  
Image Data ◽  
Remote Sensing Image ◽  
Radar Images ◽  
Texture Information ◽  
Winner Takes All ◽  
Data Browsing ◽  
Self Organizing

The goal of this paper is to present a texture clustering system for remote sensing image data. Texture information is useful for image data browsing and retrieval. Authors present the results of self-organizing neural network design for solving the clustering task of gray scale remote sensing image data. The architecture of neural network and the learning algorithms for this network such as: algorithm WTA (Winner Takes All), algorithm CWTA (Winner Takes All with Conscience) and classic Kohonen algorithm WTM (Winner Takes Most - the Winner receives more) are considered. Some experimental results using textures of the Brodatz album, multi-spectral and radar images are also represented.

scholarly journals Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Min Cao ◽  
Dongping Ming ◽  
Lu Xu ◽  
Ju Fang ◽  
Lin Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Analysis ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Texture Feature ◽  
Window Size ◽  
Remote Sensing Image ◽  
Selection Method ◽  
Image Texture ◽  
Scale Selection

Image texture is an important visual cue in image processing and analysis. Texture feature expression is an important task of geo-objects expression by using a high spatial resolution remote sensing image. Texture features based on gray level co-occurrence matrix (GLCM) are widely used in image spatial analysis where the spatial scale is especially of great significance. Based on the Fourier frequency-spectral analysis, this paper proposes an optimal scale selection method for GLCM. Different subset textures are firstly upscaled by GLCM with different window sizes. Then the multiscale texture feature images are converted into the frequency domain by Fourier transform. Consequently, the radial distribution and angular distribution curves changing with different window sizes from spectrum energy can be achieved, by which the texture window size can be selected. In order to verify the validity of this proposed texture scale selection method, this paper uses high-resolution fusion images to classify land cover based on multiscale texture expression. The results show that the proposed method combining frequency-spectral analysis-based texture scale selection can guarantee the quality and accuracy of the classification, which further proves the effectiveness of optimal texture window size selection method bases on frequency spectrum analysis. Other than scale selection in spatial domain, this paper casts a novel idea for texture scale selection in the frequency domain, which is meant for scale processing of remote sensing image.

scholarly journals HIGH-RESOLUTION REMOTE SENSING IMAGE BUILDING EXTRACTION BASED ON MARKOV MODEL

2018 ◽  
Vol XLII-3 ◽  
pp. 2411-2418 ◽  
Author(s):  
W. Zhao ◽  
L. Yan ◽  
Y. Chang ◽  
L. Gong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Markov Model ◽  
Remote Sensing Image ◽  
Image Features ◽  
Building Extraction ◽  
Texture Information ◽  
Building Information ◽  
Building Area ◽  
Image Building

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize “pseudo-buildings” in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

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