目錄

• • Multilevel Feature Fusion-Based CNN for Local Climate Zone Classification From Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
  • MSPPF-NETS: A DEEP LEARNING ARCHITECTURE FOR REMOTE SENSING IMAGE CLASSIFICATION
  • Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images
  • FUSING MULTI-SEASONAL SENTINEL-2 IMAGES WITH RESIDUAL CONVOLUTIONAL NEURAL NETWORKS FOR LOCAL CLIMATE ZONE-DERIVED URBAN LAND COVER CLASSIFICATION
  • Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks
  • Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China
  • Effective Classification of Local Climate Zones Based on Multi-Source Remote Sensing Data
  • Embranchment CNN based Local Climate Zone Classification using SAR and Multispectral Remote Sensing Data

Multilevel Feature Fusion-Based CNN for Local Climate Zone Classification From Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset

Chunping Qiu等，使用LCZ42資料集中的Sentinel-2資料，提出Sen2LCZ-Net-MF，對不同的網路訓練結果進行了比較，ResNet、DenseNet、VGG16、Xception，Sen2LCZ-Net-MF結果指標最好

C. Qiu, X. Tong, M. Schmitt, B. Bechtel and X. X. Zhu, “Multilevel Feature Fusion-Based CNN for Local Climate Zone Classification From Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset,” in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 2793-2806, 2020, doi: 10.1109/JSTARS.2020.2995711.

MSPPF-NETS: A DEEP LEARNING ARCHITECTURE FOR REMOTE SENSING IMAGE CLASSIFICATION

Yang等，使用LCZ42資料集中的Sentinel-2影像，提出以DenseNet為基本結構的MSPPF-Nets，分類精度有所提升

Yang R , Zhang Y , Zhao P , et al. MSPPF-Nets: A Deep Learning Architecture for Remote Sensing Image Classification[C]// IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019.

Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images

YOO等，將CNN與RF進行比較，對Landsat8進行分類

Yoo C , Han D , Im J , et al. Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 157(Nov.):155-170.

FUSING MULTI-SEASONAL SENTINEL-2 IMAGES WITH RESIDUAL CONVOLUTIONAL NEURAL NETWORKS FOR LOCAL CLIMATE ZONE-DERIVED URBAN LAND COVER CLASSIFICATION

Qiu，融合多個季節的sentinel-2影像，ResNet

Qiu, Chunping & Schmitt, Michael & Zhu, Xiao. (2019). Fusing Multi-Seasonal Sentinel-2 Images with Residual Convolutional Neural Networks for Local Climate Zone-Derived Urban Land Cover Classification. 5037-5040. 10.1109/IGARSS.2019.8898223.

Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks

Rosentreter等，Sentinel-2影像，CNN與RF比較

Rosentreter J , Hagensieker R , Waske B . Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks[J]. Remote Sensing of Environment, 2020, 237:111472.

Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China

Liu等，將LCZ分類視為場景分類問題，選擇中國的15個城市作為研究區域，殘差學習與SE模組結合，提出LCZNet，分析了影像塊尺寸對訓練結果的影響

Liu S , Shi Q . Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 164:229-242.

Effective Classification of Local Climate Zones Based on Multi-Source Remote Sensing Data

Jing Hao，使用SAR與多光譜影像，sentinel-1和sentinel-2，使用ResNeXT，結果說明加入SAR影像精度也只有微小的提高。

Jing H , Feng Y , Zhang W , et al. Effective Classification of Local Climate Zones Based on Multi-Source Remote Sensing Data[C]// IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019.

Embranchment CNN based Local Climate Zone Classification using SAR and Multispectral Remote Sensing Data

Feng等，基於DenseNet的雙分支CNN，使用SAR和多光譜影像，考慮到SAR與多光譜影像的成像機制不同，在不同分支內進行特徵提取