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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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.
#2 best model for Hyperspectral Image Classification on Indian Pines
Deep learning based landcover classification algorithms have recently been proposed in literature.
In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data, called HSI-CNN.
The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map.
This letter proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI classification.
With this architecture, the model gets a better performance and is more robust.
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.