High Resolution Remote Sensing Image Classification of Coastal Zone and Its Automatic Realization

2008 ◽  
Author(s):  
Yi Ma ◽  
Jie Zhang ◽  
Yufeng Gao
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Coastal Zone ◽  
Remote Sensing Image ◽  
Remote Sensing Image Classification

High Resolution Remote Sensing Image Classification Combining with Mean-Shift Segmentation and Fully Convolution Neural Network

2018 ◽  
Vol 55 (2) ◽  
pp. 022802
Author(s):  
方旭 Fang Xu ◽  
王光辉 Wang Guanghui ◽  
杨化超 Yang Huachao ◽  
刘慧杰 Liu Huijie ◽  
闫立波 Yan Libo
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Mean Shift ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Remote Sensing Image Classification

A Boundary Parallel-Like Index for High-Resolution Remotely Sensed Imagery Classification

2019 ◽  
Vol 33 (04) ◽  
pp. 1954014 ◽  
Author(s):  
Weiwei Jiang ◽  
Henglin Xiao ◽  
Zhan Zhao ◽  
Jianguo Zhou
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Remotely Sensed ◽  
Shape Features ◽  
Remote Sensing Imagery ◽  
Remotely Sensed Imagery ◽  
Remote Sensing Image Classification ◽  
Discriminating Ability

This paper proposes boundary parallel-like index (BPI) to describe shape features for high-resolution remote sensing image classification. Parallel-like boundary is found to be a discriminating clue which can reveal the shape regularity of segmented objects. Therefore, multi-orientation distance projections were constructed to measure and quantify parallel-like information. The discriminating ability was tested using original and segmented ground objects, respectively. The proposed BPI showed better discrimination for both original and segmented data than for other shape features, especially for buildings. This was also confirmed by the considerably higher accuracy of BPI in building classification experiments of high-resolution remote sensing imagery. It suggests the proposed BPI is useful for building related applications.

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