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Hyperspectral Image Classification

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 )

Papers

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Showing 131–140 of 286 papers

TitleStatusHype
Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification—0
Sparse Subspace Clustering Friendly Deep Dictionary Learning for Hyperspectral Image Classification—0
A 3D 2D convolutional Neural Network Model for Hyperspectral Image Classification—0
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification—0
Grafting Transformer on Automatically Designed Convolutional Neural Network for Hyperspectral Image ClassificationCode1
PCA-domain Fused Singular Spectral Analysis for fast and Noise-Robust Spectral-Spatial Feature Mining in Hyperspectral ClassificationCode0
Semi-Supervised Graph Prototypical Networks for Hyperspectral Image ClassificationCode1
MSHCNet: Multi-Stream Hybridized Convolutional Networks with Mixed Statistics in Euclidean/Non-Euclidean Spaces and Its Application to Hyperspectral Image Classification—0
Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image ClassificationCode1
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification—0
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