Remote sensing image texture enhancement based on HSV-BEMD algorithm

2019 ◽  
Author(s):  
Min Ma ◽  
Guoao Feng
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Texture

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 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.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Sign in / Sign up

Export Citation Format

Share Document