scholarly journals Topic Modelling for Object-Based Unsupervised Classification of VHR Panchromatic Satellite Images Based on Multiscale Image Segmentation

2017 ◽  
Vol 9 (8) ◽  
pp. 840 ◽  
Author(s):  
Li Shen ◽  
Linmei Wu ◽  
Yanshuai Dai ◽  
Wenfan Qiao ◽  
Ying Wang
Keyword(s):  
Image Segmentation ◽  
Satellite Images ◽  
Unsupervised Classification ◽  
Topic Modelling ◽  
Object Based ◽  
Multiscale Image Segmentation

scholarly journals TOPIC MODELLING FOR OBJECT-BASED CLASSIFICATION OF VHR SATELLITE IMAGES BASED ON MULTISCALE SEGMENTATIONS

2016 ◽  
Vol XLI-B7 ◽  
pp. 359-363 ◽  
Author(s):  
Li Shen ◽  
Linmei Wu ◽  
Zhipeng Li
Keyword(s):  
Satellite Images ◽  
Topic Modelling ◽  
Classification Methods ◽  
Object Class ◽  
Class Label ◽  
Similarity Comparison ◽  
Object Based ◽  
Multiscale Segmentation ◽  
Better Than

Multiscale segmentation is a key prerequisite step for object-based classification methods. However, it is often not possible to determine a sole optimal scale for the image to be classified because in many cases different geo-objects and even an identical geo-object may appear at different scales in one image. In this paper, an object-based classification method based on mutliscale segmentation results in the framework of topic modelling is proposed to classify VHR satellite images in an entirely unsupervised fashion. In the stage of topic modelling, grayscale histogram distributions for each geo-object class and each segment are learned in an unsupervised manner from multiscale segments. In the stage of classification, each segment is allocated a geo-object class label by the similarity comparison between the grayscale histogram distributions of each segment and each geo-object class. Experimental results show that the proposed method can perform better than the traditional methods based on topic modelling.

scholarly journals TOPIC MODELLING FOR OBJECT-BASED CLASSIFICATION OF VHR SATELLITE IMAGES BASED ON MULTISCALE SEGMENTATIONS

2016 ◽  
Vol XLI-B7 ◽  
pp. 359-363 ◽  
Author(s):  
Li Shen ◽  
Linmei Wu ◽  
Zhipeng Li
Keyword(s):  
Satellite Images ◽  
Topic Modelling ◽  
Classification Methods ◽  
Object Class ◽  
Class Label ◽  
Similarity Comparison ◽  
Object Based ◽  
Multiscale Segmentation ◽  
Better Than

Multiscale segmentation is a key prerequisite step for object-based classification methods. However, it is often not possible to determine a sole optimal scale for the image to be classified because in many cases different geo-objects and even an identical geo-object may appear at different scales in one image. In this paper, an object-based classification method based on mutliscale segmentation results in the framework of topic modelling is proposed to classify VHR satellite images in an entirely unsupervised fashion. In the stage of topic modelling, grayscale histogram distributions for each geo-object class and each segment are learned in an unsupervised manner from multiscale segments. In the stage of classification, each segment is allocated a geo-object class label by the similarity comparison between the grayscale histogram distributions of each segment and each geo-object class. Experimental results show that the proposed method can perform better than the traditional methods based on topic modelling.

A new web-based system for unsupervised classification of satellite images from the Google Maps engine

2012 ◽  
Author(s):  
Ángel Ferrán ◽  
Sergio Bernabé ◽  
Pablo García-Rodríguez ◽  
Antonio Plaza
Keyword(s):  
Satellite Images ◽  
Unsupervised Classification ◽  
Google Maps ◽  
Web Based ◽  
Web Based System
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