computer-vision-10
Hyperspectral Image Classification
3260 papers • 126 benchmarks • 313 datasets
Hyperspectral Image Classification is a task in the field of remote sensing and computer vision. It involves the classification of pixels in hyperspectral images into different classes based on their spectral signature. Hyperspectral images contain information about the reflectance of objects in hundreds of narrow, contiguous wavelength bands, making them useful for a wide range of applications, including mineral mapping, vegetation analysis, and urban land-use mapping. The goal of this task is to accurately identify and classify different types of objects in the image, such as soil, vegetation, water, and buildings, based on their spectral properties. ( Image credit: Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification )
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in hyperspectral-image-classification-20
Trend
Dataset
Best Model
Actions
Pavia University
Pavia University
Indian Pines
Indian Pines
Libraries
iUse these libraries to find hyperspectral-image-classification-20 models and implementations
4 papers 401
Datasets
Subtasks
No subtasks available.
Most implemented papers
Going Deeper With Contextual CNN for Hyperspectral Image Classification
Hyungtae Lee, H. Kwon•Mon Apr 11 2016
A novel deep convolutional neural network that is deeper and wider than other existing deep networks for hyperspectral image classification, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors.
876
