Hyperspectral Image Classification
92 papers with code • 8 benchmarks • 8 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 )
Benchmarks
These leaderboards are used to track progress in Hyperspectral Image Classification
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Libraries
Use these libraries to find Hyperspectral Image Classification models and implementations
Indian Pines
Pavia University
Kennedy Space Center
Salinas
Houston
Botswana
WHU-Hi
LIB-HSI
Latest papers with no code
SpectralMamba: Efficient Mamba for Hyperspectral Image Classification
Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences.
Randomized Principal Component Analysis for Hyperspectral Image Classification
The high-dimensional feature space of the hyperspectral imagery poses major challenges to the processing and analysis of the hyperspectral data sets.
Hybrid CNN Bi-LSTM neural network for Hyperspectral image classification
Use of 3-D CNN along with 2-D CNN have shown great success for learning spatial and spectral features.
An Ultralightweight Hybrid CNN Based on Redundancy Removal for Hyperspectral Image Classification
Simultaneously, for PW-Conv, we design a spectral convolution with redundancy removal (R2Spectral-Conv).
HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis
The exceptional spectral resolution of hyperspectral imagery enables material insights that are not possible with RGB or multispectral images.
DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification
First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features.
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification.
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification
Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification.
A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data
Due to the nature of the data captured by sensors that produce HSI images, a common issue is the dimensionality of the bands that may or may not contribute to the label class distinction.
Multiview Transformer: Rethinking Spatial Information in Hyperspectral Image Classification
To aggregate the multiview information, a fully-convolutional SED with a U-shape in spectral dimension is introduced to extract a multiview feature map.
