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
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.
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.
