464 citations · 475 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
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…