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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 251–260 of 286 papers

TitleStatusHype
Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification—0
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
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