Hyperspectral Image Classification
97 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
Bridging Sensor Gaps via Single-Direction Tuning for Hyperspectral Image Classification
In this paper, aiming to solve this problem, we propose the single-direction tuning (SDT) strategy, which serves as a bridge, allowing us to leverage existing labeled HSI datasets even RGB datasets to enhance the performance on new HSI datasets with limited samples.
Locality-Aware Hyperspectral Classification
Hyperspectral image classification is gaining popularity for high-precision vision tasks in remote sensing, thanks to their ability to capture visual information available in a wide continuum of spectra.
Spatial-Spectral Hyperspectral Classification based on Learnable 3D Group Convolution
leeguandong/DGCNet-for-HSI
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Deep neural networks have faced many problems in hyperspectral image classification, including the ineffective utilization of spectral-spatial joint information and the problems of gradient vanishing and overfitting that arise with increasing depth.
DGCNet: An Efficient 3D-Densenet based on Dynamic Group Convolution for Hyperspectral Remote Sensing Image Classification
leeguandong/DGCNet-for-HSI
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Referring to the idea of dynamic network, dynamic group convolution(DGC) is designed on 3d convolution kernel.
Superpixelwise Low-Rank Approximation-Based Partial Label Learning for Hyperspectral Image Classification
In this letter, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification.
Multistage Relation Network With Dual-Metric for Few-Shot Hyperspectral Image Classification
ZhaohuiXue/DM-MRN
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IEEE Transactions on Geoscience and Remote Sensing 2023
In addition, an adaptive weighting strategy is designed to fuse the obtained relation scores, and classification can be achieved by assigning each query sample to the class with the highest value of the fused relation score.
DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification
dotwang/dcn-t
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Hyperspectral image (HSI) classification is challenging due to spatial variability caused by complex imaging conditions.
SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models
The framework consists of a spectral-spatial diffusion module, and an attention-based classification module.
Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification
lms-07/AMS-M2ESL
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IEEE Transactions on Geoscience and Remote Sensing 2023
Subsequently, based on distance covariance descriptor, a dual channel distance covariance representation (DC-DCR) module is proposed for modeling unified spectral-spatial feature representations and exploring spectral-spatial relationships, especially linear and nonlinear interdependence in spectral domain.
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering
In recent years, different deep learning frameworks were introduced for hyperspectral image (HSI) classification.
