MATLAB Toolbox for Remote Sensing Change Detection

This repository contains several hyperspectral image analysis algorithms, including unmixing, registration and fusion.

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.

A collection of digital forestry tools for Matlab/Octave

codes for TNNLS paper "Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration"

codes for RS paper: Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal

Complex-valued Convolutional Neural Networks

This repository contains some basic approaches of remote sensing image processing

Morphological Building Index, extract Buildings from a high-resolution top view image.

Mohsenifar, A., Mohammadzadeh, A., Moghimi, A. and Salehi, B., 2021, A Novel Unsupervised Forest Change Detection Method Based on The Integration of a Multiresolution Singular Value Decomposition Fusion and an Edge-Aware Markov Random Field Algorithm. International Journal of Remote Sensing, doi:10.1080/01431161.2021.1995075.

(A. Moghimi, T. Celik, A. Mohammadzadeh and H. Kusetogullari, "Comparison of Keypoint Detectors and Descriptors for Relative Radiometric Normalization of Bitemporal Remote Sensing Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, doi: https://doi.org/10.1109/JSTARS.2021.3069919

MLNMF: Multilayer Nonnegative Matrix Factorization

ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image

A novel deep hashing method (DHCNN) for remote sensing image retrieval and classification, which was pulished in IEEE Trans. Geosci. Remote Sens., 2021.

Data processing, analysis and estimation utilities for a GNSS receiver array

Retrieval of plant traits from hyper- and multispectral remote sensing data with SCOPE model inversion

The demo partially associated with the following papers: "Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images" and "Multiclass Non-Randomized Spectral–Spatial Active Learning for Hyperspectral Image Classification".

These scripts are used to obtain the projected shapefiles from the original ICESat .H5 files in a batch process.

IEEE TGRS

Structural displacement monitoring using ground-based synthetic aperture radar: Implementation of 3D displacement vector