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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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 paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework.
#3 best model for Hyperspectral Image Classification on Indian Pines
Deep learning based landcover classification algorithms have recently been proposed in literature.
#2 best model for Hyperspectral Image Classification on Indian Pines
This letter proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI classification.
SOTA for Hyperspectral Image Classification on Indian Pines (using extra training data)
With this architecture, the model gets a better performance and is more robust.
#3 best model for Hyperspectral Image Classification on Pavia University
As it is very difficult and expensive to obtain class labels in real world, we integrate the proposed WCRN with AL to improve its generalization by using the most informative training samples.
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.
In order to address these issues, we aim to develop a framework for material-agnostic information retrieval in hyperspectral images based on Positive-Unlabelled (PU) classification.
Hyperspectral images (HSIs) can distinguish materials with high number of spectral bands, which is widely adopted in remote sensing applications and benefits in high accuracy land cover classifications.