537 citations · 1.6k across the 10 of their papers we have counts for
10 papers
CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
Renlong Hang, Zihao Xu, Jiuwei Zhao +3
Tropical cyclones (TCs) rank among the most destructive natural hazards, yet their forecasting faces fundamental trade-offs: numerical weather prediction (NWP) models are computati…
Unlocking Prototype Potential: An Efficient Tuning Framework for Few-Shot Class-Incremental Learning
Shengqin Jiang, Xiaoran Feng, Yuankai Qi +6
Few-shot class-incremental learning (FSCIL) seeks to continuously learn new classes from very limited samples while preserving previously acquired knowledge. Traditional methods of…
Hyperspectral Image Classification with Attention Aided CNNs
Renlong Hang, Zhu Li, Qingshan Liu +2
Convolutional neural networks (CNNs) have been widely used for hyperspectral image classification. As a common process, small cubes are firstly cropped from the hyperspectral image…
Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)
Behnood Rasti, Danfeng Hong, Renlong Hang +4
Hyperspectral images provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands) with continuous spectral informati…
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
Renlong Hang, Zhu Li, Pedram Ghamisi +3
In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (C…
Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
Renlong Hang, Qingshan Liu, Danfeng Hong +1
By considering the spectral signature as a sequence, recurrent neural networks (RNNs) have been successfully used to learn discriminative features from hyperspectral images (HSIs)…
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
Qingshan Liu, Feng Zhou, Renlong Hang +1
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature…
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
Qingshan Liu, Renlong Hang, Huihui Song +1
In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image…
Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification
Qingshan Liu, Renlong Hang, Huihui Song +3
Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the cl…
Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval
Yubao Sun, Renlong Hang, Qingshan Liu +2
In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval. First, we adopt a low rank representation (LRR) model to learn a powerful…