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strawberrybruisedetection

sts-cnn

Q. Zhang, Q. Yuan, C. Zeng, X. Li, and Y. Wei, “Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network,” IEEE TGRS, 2018.

sture2019a_minerals

Supplementary code for Sture et Al. 2019 "Obtaining Hyperspectral Signatures for Seafloor Massive Sulphide Exploration"

sudf

code for ICASSP2019 paper, Salient object detection on hyperspectral images using features learned from unsupervised segmentation task

sugardataset

The sugar dataset - A multimodal hyperspectral dataset for classification and research

super-resolution

A framework for multiframe super-resolution (enhancing the quality of an image from multiple similar low-resolution images) with support for hyperspectral imaging data.

superpca

Dimensionality reduction and classification of hyperspectral image based on SuperPCA (IEEE TGRS, 2018)

superresolution_sparserepresentation

Implements super-resolution algorithm via sparse representation of Raw patches in images

supervised-spectral-spatial-hyperspectral-image-classification-with-weighted-markov-random-fields

suprespalm

Hyperspectral Super-Resolution by Coupled Spectral Unmixing

svm_mrmr-pca

Hyperspectral image clssification using SVM classifer with hybrid feature reduction approach(mRMR-PCA)

terrautils

Python library for TERRA-REF specific modules and methods, e.g. those shared by multiple extractors.

tfdenoise

Noise reduction of hyperspectral imagery based on nonlocal tensor factorization.

the-first-pytorch-code-of-lingyou

一个能重现《Deep Learning-Based Classification of Hyperspectral Data》的代码，基于python3下的pytorch,《Deep Learning-Based Classification of Hyperspectral Data》这篇文章可以在IEEE上阅读到。如果我的小小努力能为您提供帮助，我将会无比感激。

thystle

Time-dependent HYperSpecTraL survEy

timeseries

Introduction to the R programming language for the course MATH 342 (Time series) at EPFL, Winter 2016

tlrsr

Matlab code for tensor low-rank and sparse representation learning.

tpe-3d-cnn

Spectral–Spatial Classification of Hyperspectral Image Based on Self-adaptive Deep Residual 3D Convolutional Neural Network

tproduct

tensor-tensor product toolbox

transfer-learning-for-hsi

Graduation project.

transform_learning_hyperspectral_image_classification

Transform Learning: Analysis Dictionary Learning

tree-species-classification-based-on-the-combination-of-hyperspectral-and-airborne-lidar

Hyperspectral remote sensing images have high spectral resolution, but they can only provide two-dimensional spatial information, and some materials may have similar spectrum. In addition, hyperspectral data has high redundancy, and the classification accuracy is reduced due to the Hughes phenomenon. LiDAR can provide reliable three-dimensional data and forest canopy characteristics.The code mainly includes single tree segmentation, feature extraction, feature importance analysis, KNN and SVM classification. The method proposed in the article was verified, and satisfactory experimental results were obtained.The overall classification accuracy is over 85$\%$, which is about 10$\%$ higher than the classification accuracy of single hyperspectral data.

treecrowndelineation

Delineate tree crowns from AOP camera and hyperspectral data

tutorial-ugm-hyperspectral

A Tutorial on Modeling and Inference in Undirected Graphical Models for Hyperspectral Image Analysis

tutorials

Learn to use TERRA REF data and software

two-branch-cnn-multisource-rs-classification

undip

UnDIP: Hyperspectral Unmixing Using Deep Image Prior

unmixing-plmm

Hyperspectral unmixing with spectral variability using a perturbed linear mixing model

unmixing-ssp

Unmixing hyperspectral image using algorithms published in SSP by O. Eches

unmixing_spectral_variability

A complete toolbox for spectral unmixing with spectral variability