Urban land cover classification from high resolution multi-spectral IKONOS imagery

2003 ◽  
Author(s):  
C.H. Davis ◽  
Xiangyun Wang
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
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.

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 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

scholarly journals Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3717 ◽  
Author(s):  
Pengbin Zhang ◽  
Yinghai Ke ◽  
Zhenxin Zhang ◽  
Mingli Wang ◽  
Peng Li ◽  
...  
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Land Cover Classification ◽  
Urban Land ◽  
Learning Models ◽  
Multi Scale ◽  
Urban Land Cover

Urban land cover and land use mapping plays an important role in urban planning and management. In this paper, novel multi-scale deep learning models, namely ASPP-Unet and ResASPP-Unet are proposed for urban land cover classification based on very high resolution (VHR) satellite imagery. The proposed ASPP-Unet model consists of a contracting path which extracts the high-level features, and an expansive path, which up-samples the features to create a high-resolution output. The atrous spatial pyramid pooling (ASPP) technique is utilized in the bottom layer in order to incorporate multi-scale deep features into a discriminative feature. The ResASPP-Unet model further improves the architecture by replacing each layer with residual unit. The models were trained and tested based on WorldView-2 (WV2) and WorldView-3 (WV3) imageries over the city of Beijing. Model parameters including layer depth and the number of initial feature maps (IFMs) as well as the input image bands were evaluated in terms of their impact on the model performances. It is shown that the ResASPP-Unet model with 11 layers and 64 IFMs based on 8-band WV2 imagery produced the highest classification accuracy (87.1% for WV2 imagery and 84.0% for WV3 imagery). The ASPP-Unet model with the same parameter setting produced slightly lower accuracy, with overall accuracy of 85.2% for WV2 imagery and 83.2% for WV3 imagery. Overall, the proposed models outperformed the state-of-the-art models, e.g., U-Net, convolutional neural network (CNN) and Support Vector Machine (SVM) model over both WV2 and WV3 images, and yielded robust and efficient urban land cover classification results.

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