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.
1 This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deep learning techniques to their own dataset.
This letter proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI classification.
Ranked #1 on Hyperspectral Image Classification on Indian Pines (using extra training data)
Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations.
Deep learning based landcover classification algorithms have recently been proposed in literature.
Ranked #4 on Hyperspectral Image Classification on Indian Pines
The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques.
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.
Ranked #5 on Hyperspectral Image Classification on Indian Pines
In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification.
Ranked #1 on Hyperspectral Image Classification on Salinas
Hyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands.