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 with no code
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.
Multi-Scale U-Shape MLP for Hyperspectral Image Classification
Hyperspectral images have significant applications in various domains, since they register numerous semantic and spatial information in the spectral band with spatial variability of spectral signatures.
Forward-Forward Algorithm for Hyperspectral Image Classification: A Preliminary Study
The back-propagation algorithm has long been the de-facto standard in optimizing weights and biases in neural networks, particularly in cutting-edge deep learning models.
SaaFormer: Spectral-spatial Axial Aggregation Transformer for Hyperspectral Image Classification
Consequently, the pixel-wise random sampling approach poses a risk of data leakage.
When Hyperspectral Image Classification Meets Diffusion Models: An Unsupervised Feature Learning Framework
Learning effective spectral-spatial features is important for the hyperspectral image (HSI) classification task, but the majority of existing HSI classification methods still suffer from modeling complex spectral-spatial relations and characterizing low-level details and high-level semantics comprehensively.
Sharpend Cosine Similarity based Neural Network for Hyperspectral Image Classification
2D Convolutional Neural Networks (CNN) emerged as a viable network whereas, 3D CNNs are a better alternative due to accurate classification.
Multimodal Hyperspectral Image Classification via Interconnected Fusion
More specifically, they overlook the contextual information across modalities of HSI and LiDAR and the intra-modality characteristics of LiDAR.
A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification
To address the above issues, we propose a multi-stage search architecture in order to overcome asymmetric spectral-spatial dimensions and capture significant features.
Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification
This framework comprises two main parts: (i) a leakage-free balanced sampling strategy, and (ii) a modified end-to-end fully convolutional network (FCN) architecture that optimizes the trade-off between accuracy and efficiency.
