Hyperspectral Image Classification
93 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
Importance of Disjoint Sampling in Conventional and Transformer Models for Hyperspectral Image Classification
This paper presents an innovative disjoint sampling approach for training SOTA models on Hyperspectral image classification (HSIC) tasks.
Pyramid Hierarchical Transformer for Hyperspectral Image Classification
The traditional Transformer model encounters challenges with variable-length input sequences, particularly in Hyperspectral Image Classification (HSIC), leading to efficiency and scalability concerns.
Traditional to Transformers: A Survey on Current Trends and Future Prospects for Hyperspectral Image Classification
Hyperspectral image classification is a challenging task due to the high dimensionality and complex nature of hyperspectral data.
3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification
shyamvarahagiri/3d-convsst
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Furthermore, to have high classification performance, there should be a strong interaction between the HSI token and the class (CLS) token.
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.
A Universal Knowledge Embedded Contrastive Learning Framework for Hyperspectral Image Classification
Therefore, we propose a universal knowledge embedded contrastive learning framework (KnowCL) for supervised, unsupervised, and semisupervised HSI classification, which largely closes the gap of HSI classification models between pocket models and standard vision backbones.
Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification
henulwy/apnt
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However, observing the classification results of existing methods, we found that boundary patches corresponding to the pixels which are located at the boundary of the objects in the hyperspectral images, are hard to classify.
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding.
Attention based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image Classification
mqalkhatib/Real_Complex_Classification
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Experimental evidence show that SE block improves the models overall accuracy by almost 1\%.
Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification
henulwy/stbdip
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In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples.
