Spectral classification using convolutional neural networks
There is a great need for accurate and autonomous spectral classification methods in astrophysics. This thesis is about training a convolutional neural network (ConvNet) to recognize an object class (quasar, star or galaxy) from one-dimension spectra only. Author developed several scripts and C programs for datasets preparation, preprocessing and postprocessing of the data. EBLearn library (developed by Pierre Sermanet and Yann LeCun) was used to create ConvNets. Application on dataset of more than 60000 spectra yielded success rate of nearly 95%. This thesis conclusively proved great potential of convolutional neural networks and deep learning methods in astrophysics.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples
Aiming at the problem that the manual labeling of samples in the hyperspectral image classification is expensive and laborious, a large number of unlabeled samples are not effectively utilized and the classification resu…
Active LearningClassificationFew-Shot Image ClassificationHyperspectral Image Classification+2A Convolutional Neural Network with Mapping Layers for Hyperspectral Image Classification
In this paper, we propose a convolutional neural network with mapping layers (MCNN) for hyperspectral image (HSI) classification. The proposed mapping layers map the input patch into a low dimensional subspace by multili…
ClassificationGeneral ClassificationHyperspectral Image Classificationimage-classification+1Going Deeper with Contextual CNN for Hyperspectral Image Classification
In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in …
ClassificationGeneral ClassificationHyperspectral Image Classificationimage-classification+1CMTNet: Convolutional Meets Transformer Network for Hyperspectral Images Classification
Hyperspectral remote sensing (HIS) enables the detailed capture of spectral information from the Earth's surface, facilitating precise classification and identification of surface crops due to its superior spectral diagn…
ClassificationCrop ClassificationDiagnosticAdaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification
Recently, graph convolutional networks (GCNs) have been developed to explore spatial relationship between pixels, achieving better classification performance of hyperspectral images (HSIs). However, these methods fail to…
Graph AttentionHyperspectral Image Classificationimage-classificationImage Classification