A novel approach for denoising coloured remote sensing image using Legendre Fenchel Transformation

2014 ◽  
Author(s):  
Santhosh S. ◽  
Abinaya N. ◽  
Rashmi G. ◽  
Sowmya V. ◽  
Soman K.P.
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Novel Approach

Coastline Detection from Remote Sensing Image Based on K-Mean Cluster and Distance Transform Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1567-1571 ◽  
Author(s):  
Ying Liu
Keyword(s):  
Remote Sensing ◽  
Cluster Algorithm ◽  
Remote Sensing Image ◽  
Distance Transform ◽  
Land Area ◽  
Remote Sensing Technique ◽  
Novel Approach ◽  
Sea Area

Using remote sensing technique to determine coastline's position has been received vital attention. This paper presents a novel approach for detecting coastline of remote sensing image based on K-Means cluster and Distance Transform algorithm. K-Means cluster algorithm divides the image into two regions-water and land area. Then to extract the sea area by distance transfoming. Finally, the coastline will be detected by edge traking. Results showed that the method proposed in this paper have good performance in accuracy and completeness.

A User Preferences Modeling for Intellegent Dissemination of Remote Sensing Image

2014 ◽  
Vol 519-520 ◽  
pp. 548-552
Author(s):  
Chun Hui Zhou ◽  
Gou Jun Luo ◽  
Di Chen ◽  
Yu Xia ◽  
Li Wen Huang
Keyword(s):  
Remote Sensing ◽  
Time Factors ◽  
User Profile ◽  
Remote Sensing Image ◽  
Computing Methods ◽  
User Preferences ◽  
Time Space ◽  
Novel Approach ◽  
User Demand ◽  
Demand Preference

In order to achieve the intelligent dissemination of remote sensing image, the primary task is to establish a suitable user profile. In this paper, we proposed a novel approach of modeling user demand preferences, and took into account the multiple interests and the time factors. And we presented some computing methods of feature preference including time, space, image parameters, etc. At last, the simulation example shows the feasibility and effectiveness of the designed user demand preference model.

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. 

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