5shark

DeepLearnToolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

pytorch-tutorial

PyTorch深度学习快速入门教程（绝对通俗易懂！）

gcForest

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

GLCM

本实验的主要目的是基于遥感图像计算灰度共生矩阵，并基于该矩阵计算多种纹理特征。所有的计算结果已与ENVI结果进行对比，实验结果一致。

IEEE_TGRS_MDL-RS

Danfeng Hong, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, Bing Zhang. More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification, IEEE TGRS, 2021, 59(5): 4340-4354.

ISPRS_S2FL

Danfeng Hong, JIngliang Hu, Jing Yao, Jocelyn Chanussot, Xiao Xiang Zhu. Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model, ISPRS JP&RS, 2021.

Multimodal-Remote-Sensing-Toolkit

A python tool to perform deep learning experiments on multimodal remote sensing data.

CNN-LSTM_for_DSM

Using CNN-LSTM deep learning model for digital soil mapping. This is the code for paper "Zhang et al. A CNN-LSTM model for soil organic carbon content prediction with long time series of MODIS-based phenological variables"

IEEE_GRSL_EndNet

Danfeng Hong, Lianru Gao, Renlong Hang, Bing Zhang, Jocelyn Chanussot. Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR Data, IEEE GRSL, 2020.

IEEE_TGRS_CCR-Net

Xin Wu, Danfeng Hong, Jocelyn Chanussot. Convolutional Neural Networks for Multimodal Remote Sensing Data Classification, IEEE Transactions on Geoscience and Remote Sensing, 2021.

AM3Net_Multimodal_Data_Fusion

Code for J. Wang, J. Li, Y. Shi, J. Lai and X. Tan, "AM3Net: Adaptive Mutual-learning-based Multimodal Data Fusion Network," in IEEE TCSVT, 2022. We conducted the experiments on the hyperspectral and lidar dataset(Houston and Trento) and multispectral and synthetic aperture radar data (grss-dfc-2007 datasets).

-MAHiDFNet

Multi-attentive hierarchical dense fusion net for fusion classification of hyperspectral and LiDAR data

HRWN

This example implements the paper in review [Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture]

palsar_gedi_agb

Wall-to-Wall Above-ground Biomass Estimation with ALOS-2 PALSAR-2 L-Band SAR Data and GEDI

AboveGroundBiomassEstimation

RNPRF-RNDFF-RNPMF

Carbon-Trading-Verfication

co2go

In this project we will create a model to predict Carbon Storage in a Forest

machine-learning-workflow-for-carbon-assessment

Sciknow'19 paper - Semantic Workflows and Machine Learning for the Assessment of Carbon Storage by Urban Trees