scholarly journals Attention-based DenseNet network for multi-source remote sensing classification

2021 ◽  
Vol 865 (1) ◽  
pp. 012002
Author(s):  
Lishuo Zhang ◽  
Hong Lin ◽  
Fanyang Zeng
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Classification

A spatial-channel progressive fusion ResNet for remote sensing classification

2020 ◽  
Author(s):  
Hao Zhu ◽  
Mengru Ma ◽  
Wenping Ma ◽  
Licheng Jiao ◽  
Shikuan Hong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Classification

Based on IDRISI Remote Sensing Images Land-Use Types of Supervised Classification Techniques

2013 ◽  
Vol 415 ◽  
pp. 305-308
Author(s):  
Kun Zhang ◽  
Hai Feng Wang ◽  
Zhuang Li
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Classification Techniques ◽  
Analysis And Evaluation ◽  
Remote Sensing Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Remote Sensing Image Classification ◽  
Land Use Types ◽  
Area Distribution

With remote sensing technology and computer technology, remote sensing classification technology has been rapid progress. In the traditional classification of remote sensing technology, based on the combination of today's technology in the field of remote sensing image classification, some new developments and applications for land cover classification techniques to make more comprehensive elaboration. Using the minimum distance classifier extracts of the study area land use types. Ultimately extracted land use study area distribution image and make its analysis and evaluation.

scholarly journals A Hybrid Model Integrating Spatial Pattern, Spatial Correlation, and Edge Information for Image Classification

2019 ◽  
Vol 11 (13) ◽  
pp. 1599 ◽  
Author(s):  
Yunwei Tang ◽  
Linhai Jing ◽  
Fan Shi ◽  
Xiao Li ◽  
Fang Qiu
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Correlation ◽  
Hybrid Model ◽  
General Distribution ◽  
Multiple Point ◽  
Spatial Covariance ◽  
Edge Preserving ◽  
Remote Sensing Classification ◽  
Different Sources

This paper develops a novel hybrid model that integrates three spatial contexts into probabilistic classifiers for remote sensing classification. First, spatial pattern is introduced using multiple-point geostatistics (MPGs) to characterize the general distribution and arrangement of land covers. Second, spatial correlation is incorporated using spatial covariance to quantify the dependence between pixels. Third, an edge-preserving filter based on the Sobel mask is introduced to avoid the over-smoothing problem. These three types of contexts are combined with the spectral information from the original image within a higher-order Markov random field (MRF) framework for classification. The developed model is capable of classifying complex and diverse land cover types by allowing effective anisotropic filtering of the image while retaining details near edges. Experiments with three remote sensing images from different sources based on three probabilistic classifiers obtained results that significantly improved classification accuracies when compared with other popular contextual classifiers and most state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document