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.

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.

scholarly journals Large-Area Full-Coverage Remote Sensing Image Collection Filtering Algorithm for Individual Demands

2021 ◽  
Vol 13 (23) ◽  
pp. 13475
Author(s):  
Boce Chu ◽  
Feng Gao ◽  
Yingte Chai ◽  
Yu Liu ◽  
Chen Yao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Image Filtering ◽  
Remote Sensing Image ◽  
User Preferences ◽  
Large Area ◽  
Filtering Algorithm ◽  
Full Coverage ◽  
Filtering Effect ◽  
Image Collection

Remote sensing is the main technical means for urban researchers and planners to effectively observe targeted urban areas. Generally, it is difficult for only one image to cover a whole urban area and one image cannot support the demands of urban planning tasks for spatial statistical analysis of a whole city. Therefore, people often artificially find multiple images with complementary regions in an urban area on the premise of meeting the basic requirements for resolution, cloudiness, and timeliness. However, with the rapid increase of remote sensing satellites and data in recent years, time-consuming and low performance manual filter results have become more and more unacceptable. Therefore, the issue of efficiently and automatically selecting an optimal image collection from massive image data to meet individual demands of whole urban observation has become an urgent problem. To solve this problem, this paper proposes a large-area full-coverage remote sensing image collection filtering algorithm for individual demands (LFCF-ID). This algorithm achieves a new image filtering mode and solves the difficult problem of selecting a full-coverage remote sensing image collection from a vast amount of data. Additionally, this is the first study to achieve full-coverage image filtering that considers user preferences concerning spatial resolution, timeliness, and cloud percentage. The algorithm first quantitatively models demand indicators, such as cloudiness, timeliness, resolution, and coverage, and then coarsely filters the image collection according to the ranking of model scores to meet the different needs of different users for images. Then, relying on map gridding, the image collection is genetically optimized for individuals using a genetic algorithm (GA), which can quickly remove redundant images from the image collection to produce the final filtering result according to the fitness score. The proposed method is compared with manual filtering and greedy retrieval to verify its computing speed and filtering effect. The experiments show that the proposed method has great speed advantages over traditional methods and exceeds the results of manual filtering in terms of filtering effect.

A Natural Language User Demand Semantic Model for Remote Sensing Image Retrieval

2012 ◽  
Vol 241-244 ◽  
pp. 2897-2900
Author(s):  
Xia Zhang ◽  
Liu Yuan Chen ◽  
Xin Yan Zhu
Keyword(s):  
Remote Sensing ◽  
Natural Language ◽  
Image Retrieval ◽  
Semantic Analysis ◽  
Research Work ◽  
Remote Sensing Image ◽  
Semantic Model ◽  
Semantic Relationship ◽  
Language User ◽  
User Demand

Remote sensing (RS) image can be applied in many domains. Most research work on RS image retrieval is to meet the demand of professional user. However, there are demands for RS image that comes from non-professional users who propose the requests in natural language (NL) not filling in professional request forms. Some problems are needed to be solved to implement RS image retrieval based on NL user demand. The objective of this research was to propose a user demand semantic model to solve the problem of translation from NL user demand to value requirements. Based on plenty of materials investigated in application domains, the syntax and semantics of NL user demand was analyzed. Semantic relationship is summarized in terms of the semantic analysis. After that, a user demand semantic model is proposed and built with ontology. It can be conclude that the proposed semantic model may help to RS image retrieval based on NL user demand.

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