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Hyperspectral Image Classification

4 papers with code · Computer Vision

Hyperspectral image classification is the task of classifying a class label to every pixel in an image that was captured using (hyper)spectral sensors.

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Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network

1 May 2017xiangyongcao/CNN_HSIC_MRF

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective.


BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

1 Dec 2016kaustubh0mani/BASS-Net

Deep learning based landcover classification algorithms have recently been proposed in literature. In hyperspectral images (HSI) they face the challenges of large dimensionality, spatial variability of spectral signatures and scarcity of labeled data.


Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification

30 Oct 2018codeRimoe/DL_for_RSIs

Therefore, considering the spectra as sequences, recurrent neural networks (RNNs) have been applied in HSI classification, for RNNs is skilled at dealing with sequential data. With this architecture, the model gets a better performance and is more robust.


Hyperspectral Image Classification in the Presence of Noisy Labels

12 Sep 2018junjun-jiang/RLPA

However, current classification methods all ignore an important and inevitable problem---labels may be corrupted and collecting clean labels for training samples is difficult, and often impractical. The key idea of RLPA is to exploit knowledge (e.g., the superpixel based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation.