Publicaciones en colaboración con investigadores/as de Soochow University (22)

2022

  1. Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15, pp. 6804-6816

  2. Decoding the Partial Pretrained Networks for Sea-Ice Segmentation of 2021 Gaofen Challenge

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15, pp. 4521-4530

  3. Deep Learning-Based Building Footprint Extraction with Missing Annotations

    IEEE Geoscience and Remote Sensing Letters, Vol. 19

  4. DisOptNet: Distilling Semantic Knowledge From Optical Images for Weather-Independent Building Segmentation

    IEEE Transactions on Geoscience and Remote Sensing, Vol. 60

  5. Multisource Data Reconstruction-Based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval

    IEEE Transactions on Geoscience and Remote Sensing, Vol. 60

  6. Rotation-Invariant Deep Embedding for Remote Sensing Images

    IEEE Transactions on Geoscience and Remote Sensing, Vol. 60

  7. SAR Time-Series Despeckling via Nonlocal Total Variation Regularized Robust PCA

    IEEE Geoscience and Remote Sensing Letters, Vol. 19

  8. Toward Tightness of Scalable Neighborhood Component Analysis for Remote-Sensing Image Characterization

    IEEE Geoscience and Remote Sensing Letters, Vol. 19

2021

  1. Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images with Label Noise

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, pp. 2551-2562

  2. PiCoCo: Pixelwise Contrast and Consistency Learning for Semisupervised Building Footprint Segmentation

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, pp. 10548-10559

  3. ROBUST DEEP METRIC LEARNING FOR REMOTE SENSING IMAGES WITH NOISY ANNOTATIONS

    International Geoscience and Remote Sensing Symposium (IGARSS)

  4. Rice-yield prediction with multi-temporal sentinel-2 data and 3D CNN: A case study in Nepal

    Remote Sensing, Vol. 13, Núm. 7

  5. Robust Normalized Softmax Loss for Deep Metric Learning-Based Characterization of Remote Sensing Images with Label Noise

    IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, Núm. 10, pp. 8798-8811

  6. Sentinel-3 Super-Resolution Based on Dense Multireceptive Channel Attention

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, pp. 7359-7372