scholarly journals Textural features from wavelets on graphs for very high resolution panchromatic Pléiades image classification

2014 ◽  
pp. 131-136
Author(s):  
Minh Tan Pham ◽  
Grégoire Mercier ◽  
Julien Michel
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Textural Features ◽  
Nous Proposons ◽  
Haute Résolution ◽  
Very High

Dans cet article, nous proposons une méthode de caractérisation locale des textures des images de très haute résolution spatiale, dans lesquelles l'hypothèse de stationnarité est peu respectée.Une approche ponctuelle (i.e. non-dense) est d'abord introduite pour la représentation de l'image en utilisant un ensemble de pixels d'intétêt au lieu de la totalité des pixels de l'image. Un graphe pondéré est ensuite construit à partir de ces pixels représentatifs. Le signal de texture, porté sur ce graphe, est ensuite analysé à travers une transformée en ondelettes sur graphe. La classification en texture, implémentée ici de façon non-supervisée, est donc réalisée par la classification des coefficients d'ondelettes sur le graphe. Les expérimentations appliquées aux images panchromatiques Pléiades nous donnent des résultats très prometteurs avec une bonne précision de classification tout en gardant une compléxité intéressante.

scholarly journals A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1451-1454 ◽  
Author(s):  
C. Zhang ◽  
X. Pan ◽  
S. Q. Zhang ◽  
H. P. Li ◽  
P. M. Atkinson
Keyword(s):  
High Resolution ◽  
Decision Tree ◽  
Image Classification ◽  
Rough Set ◽  
Remotely Sensed ◽  
Classification Performance ◽  
Learning Approaches ◽  
Remotely Sensed Data ◽  
Remotely Sensed Image ◽  
Very High

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. <br><br> This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

scholarly journals Hierarchical Multi-View Semi-Supervised Learning for Very High-Resolution Remote Sensing Image Classification

2020 ◽  
Vol 12 (6) ◽  
pp. 1012 ◽  
Author(s):  
Cheng Shi ◽  
Zhiyong Lv ◽  
Xiuhong Yang ◽  
Pengfei Xu ◽  
Irfana Bibi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Supervised Learning ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification ◽  
Very High

Traditional classification methods used for very high-resolution (VHR) remote sensing images require a large number of labeled samples to obtain higher classification accuracy. Labeled samples are difficult to obtain and costly. Therefore, semi-supervised learning becomes an effective paradigm that combines the labeled and unlabeled samples for classification. In semi-supervised learning, the key issue is to enlarge the training set by selecting highly-reliable unlabeled samples. Observing the samples from multiple views is helpful to improving the accuracy of label prediction for unlabeled samples. Hence, the reasonable view partition is very important for improving the classification performance. In this paper, a hierarchical multi-view semi-supervised learning framework with CNNs (HMVSSL) is proposed for VHR remote sensing image classification. Firstly, a superpixel-based sample enlargement method is proposed to increase the number of training samples in each view. Secondly, a view partition method is designed to partition the training set into two independent views, and the partitioned subsets are characterized by being inter-distinctive and intra-compact. Finally, a collaborative classification strategy is proposed for the final classification. Experiments are conducted on three VHR remote sensing images, and the results show that the proposed method performs better than several state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document