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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 251–275 of 286 papers

TitleStatusHype
Hyperspectral image classification using spectral-spatial LSTMs—0
Wide Contextual Residual Network with Active Learning for Remote Sensing Image ClassificationCode0
Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations—0
Hyperspectral image classification via a random patches networkCode0
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification—0
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral ImageCode0
Unsupervised Band Selection of Hyperspectral Images via Multi-dictionary Sparse Representation—0
Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification—0
Cross-domain CNN for Hyperspectral Image Classification—0
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning settingCode0
Does Normalization Methods Play a Role for Hyperspectral Image Classification?—0
Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classification—0
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification—0
Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost TuningCode0
Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural NetworkCode0
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image ClassificationCode1
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural NetworkCode0
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image ClassificationCode0
Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification—0
Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions—0
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach—0
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings—0
On the Sampling Strategy for Evaluation of Spectral-spatial Methods in Hyperspectral Image Classification—0
Going Deeper with Contextual CNN for Hyperspectral Image ClassificationCode0
Deep supervised learning for hyperspectral data classification through convolutional neural networksCode0
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