Machine learning approaches for high-resolution urban land cover classification

2011 ◽  
Author(s):  
Ranga Raju Vatsavai ◽  
Eddie Bright ◽  
Chandola Varun ◽  
Bhaduri Budhendra ◽  
Anil Cheriyadat ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Learning Approaches ◽  
Urban Land Cover

scholarly journals Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

2020 ◽  
Vol 12 (2) ◽  
pp. 311 ◽  
Author(s):  
Chun Liu ◽  
Doudou Zeng ◽  
Hangbin Wu ◽  
Yin Wang ◽  
Shoujun Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Small Sample ◽  
Connectivity Pattern ◽  
Feature Maps ◽  
Convolutional Network ◽  
Urban Land Cover

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.

scholarly journals Land Cover Classification using Machine Learning Approaches from High Resolution Images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akhtar Jamil ◽  
Aftab Ahmed Khan ◽  
Alaa Ali Hameed ◽  
Sibghat Sibghat Ullah Bazai
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Learning Approaches ◽  
High Resolution Images

Urban land cover classification with high-resolution polarimetric SAR interferometric data

2010 ◽  
Vol 36 (3) ◽  
pp. 236-247 ◽  
Author(s):  
Xinwu Li ◽  
Eric Pottier ◽  
Huadong Guo ◽  
Laurent Ferro-Famil
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Polarimetric Sar ◽  
Urban Land Cover

scholarly journals Land Cover Classification using Machine Learning Approaches from High Resolution Images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akhtar Jamil ◽  
Aftab Ahmed Khan ◽  
Alaa Ali Hameed ◽  
Sibghat Sibghat Ullah Bazai
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Learning Approaches ◽  
High Resolution Images

scholarly journals A Comparison of Two Morphological Techniques in the Classification of Urban Land Cover

2020 ◽  
Vol 12 (7) ◽  
pp. 1089
Author(s):  
Lesiba Thomas Tsoeleng ◽  
John Odindi ◽  
Paidamwoyo Mhangara
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Land Cover Classification ◽  
Urban Land ◽  
Analysis Techniques ◽  
Urban Land Cover ◽  
Image Analysis Techniques

Understanding the often-heterogeneous land cover in urban areas is critical for, among other things, environmental monitoring, spatial planning, and enforcement. Recently, several earth observation satellites were developed with an enhanced spatial resolution that provides for precise and detailed representations of image objects. Morphological image analysis techniques provide useful tools for extracting spatial features from high-resolution, remotely sensed images. This study investigated the efficacy of mathematical morphological (MM) techniques in the land cover classification of a heterogeneous urban landscape using very high-resolution pan-sharpened Pleiades imagery. Specifically, the study evaluated two morphological profiles (MP) techniques (i.e., concatenation of morphological profiles (CMPs) and multi-morphological profiles (MMPs)) in the classification of a heterogeneous urban land cover. The overall accuracies for CMP were 83.14% and 83.19% over the two study areas. Similarly, the MMP overall accuracies were 84.42% and 84.08% for the two study sites. The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes. In general, similar and visually acceptable results were produced for land cover classification using either CMP or MMP image analysis techniques

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