Exploration and Enhancement of Land Management Strategies Using Hyperspectral Imagery and Big Data Analytics

ZHANG, HUAIZHONG (PI)
TROVATI, MARCELLO (CoI)

Computer Science

Status	Finished
Effective start/end date	12/10/20 → 31/10/21

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

1 Article (journal)

Adaptive MUlti-scale Superpixel Embedding Convolutional Neural Network (AMUSE-CNN) for Land Use Classification
ZHANG, HUAIZHONG., Altham, C., TROVATI, MARCELLO., Zhang, C., Ajienka, N., Lawal, O. & NWEGBU, CHIDOZIE., 7 Sept 2022, (E-pub ahead of print) In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12 p.
Research output: Contribution to journal › Article (journal) › peer-review

Open Access
File
8 Downloads (Pure)

Exploration and Enhancement of Land Management Strategies Using Hyperspectral Imagery and Big Data Analytics

Project Details

UN Sustainable Development Goals

Fingerprint

Adaptive MUlti-scale Superpixel Embedding Convolutional Neural Network (AMUSE-CNN) for Land Use Classification

Exploration and Enhancement of Land Management Strategies Using Hyperspectral Imagery and Big Data Analytics

Project Details

UN Sustainable Development Goals

Fingerprint

Research output

Adaptive MUlti-scale Superpixel Embedding Convolutional Neural Network (AMUSE-CNN) for Land Use Classification