Search Results for author: Renlong Hang

Found 11 papers, 4 papers with code

Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR Data

1 code implementation IEEE Geoscience and Remote Sensing Letters 2020 Danfeng Hong, Lianru Gao, Renlong Hang, Bing Zhang, Jocelyn Chanussot

To overcome this limitation, we present a simple but effective multimodal DL baseline by following a deep encoder–decoder network architecture, EndNet for short, for the classification of hyperspectral and light detection and ranging (LiDAR) data.

Classification Decoder

Hyperspectral Image Classification with Attention Aided CNNs

1 code implementation25 May 2020 Renlong Hang, Zhu Li, Qingshan Liu, Pedram Ghamisi, Shuvra S. Bhattacharyya

Specifically, a spectral attention sub-network and a spatial attention sub-network are proposed for spectral and spatial classification, respectively.

Classification General Classification +1

Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)

1 code implementation5 Mar 2020 Behnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson

The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques.

General Classification Hyperspectral Image Classification

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

no code implementations4 Feb 2020 Renlong Hang, Zhu Li, Pedram Ghamisi, Danfeng Hong, Guiyu Xia, Qingshan Liu

For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy.

Classification General Classification

Oceanic Eddy Identification Using an AI Scheme

no code implementations Remote Sensing 2019 Guangjun Xu, Cheng Cheng, Wenxian Yang, Wenhong Xie, Lingmei Kong, Renlong Hang, Furong Ma, Changming Dong, Jingsong Yang

Oceanic eddies play an important role in global energyand material transport, and contribute greatly to nutrient and phytoplankton distribution.

Scene Parsing

Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

no code implementations28 Feb 2019 Renlong Hang, Qingshan Liu, Danfeng Hong, Pedram Ghamisi

The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from non-adjacent spectral bands.

Classification General Classification +1

Hyperspectral image classification using spectral-spatial LSTMs

no code implementations20 Aug 2018 Feng Zhou, Renlong Hang, Qingshan Liu, Xiaotong Yuan

Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to learn the spectral feature.

Classification General Classification +1

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

1 code implementation23 Mar 2017 Qingshan Liu, Feng Zhou, Renlong Hang, Xiao-Tong Yuan

In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it.

General Classification Hyperspectral Image Classification

Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification

no code implementations11 Nov 2016 Qingshan Liu, Renlong Hang, Huihui Song, Fuping Zhu, Javier Plaza, Antonio Plaza

In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification.

Classification General Classification +3

Graph Regularized Low Rank Representation for Aerosol Optical Depth Retrieval

no code implementations22 Feb 2016 Yubao Sun, Renlong Hang, Qingshan Liu, Fuping Zhu, Hucheng Pei

In this paper, we propose a novel data-driven regression model for aerosol optical depth (AOD) retrieval.

regression Retrieval

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