Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection

نویسندگان
چکیده

منابع مشابه

Semi-supervised feature learning for hyperspectral image classification

Hyperspectral image has high-dimensional Spectral–spatial features, those features with some noisy and redundant information. Since redundant features can have significant adverse effect on learning performance. So efficient and robust feature selection methods are make the best of labeled and unlabeled points to extract meaningful features and eliminate noisy ones. On the other hand, obtaining...

متن کامل

A novel semi-supervised learning framework for hyperspectral image classification

In this paper, we propose a novel semi-supervised learning classification framework using box-based smooth ordering and Multiple 1D-embedding-based interpolation method in Ref. 25 for hyperspectral images. Due to the lack of labeled samples, conventional supervised approaches cannot generally perform efficient enough. On the other hand, obtaining labeled samples for hyperspectral image classifi...

متن کامل

Hyperspectral Image Classification Based on Semi-Supervised Rotation Forest

Ensemble learning is widely used to combine varieties of weak learners in order to generate a relatively stronger learner by reducing either the bias or the variance of the individual learners. Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by promoting the diversity of base classifiers since...

متن کامل

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generativ...

متن کامل

Semi-Supervised Based Hyperspectral Imagery Classification

Hyperspectral imagery classification is a challenging problem. Wherein, the high number of spectral channels and the high cost of true sample labeling greatly reduce the classification precision. In this paper, we proposed a semi-supervised method, which combine linear discriminant analysis and manifold learning, to improve the precision of hyperspectral imagery classification. Experimental res...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Remote Sensing

سال: 2018

ISSN: 2072-4292

DOI: 10.3390/rs10040574