scholarly journals A Hyperspectral Image Classification Framework with Spatial Pixel Pair Features

Sensors ◽  
2017 ◽  
Vol 17 (10) ◽  
pp. 2421 ◽  
Author(s):  
Lingyan Ran ◽  
Yanning Zhang ◽  
Wei Wei ◽  
Qilin Zhang
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Classification Framework ◽  
Pixel Pair

scholarly journals A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

2019 ◽  
Vol 11 (17) ◽  
pp. 2057 ◽  
Author(s):  
Majid Shadman Roodposhti ◽  
Arko Lucieer ◽  
Asim Anees ◽  
Brett Bryan
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Mean Shift ◽  
Training Sample ◽  
Uncertainty Assessment ◽  
Hyperspectral Image Classification ◽  
Rule Based ◽  
Classification Framework ◽  
Rule Sets ◽  
Ensemble Algorithms

This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.

scholarly journals Semi-Supervised Hyperspectral Image Classification via Spatial-Regulated Self-Training

2020 ◽  
Vol 12 (1) ◽  
pp. 159 ◽  
Author(s):  
Yue Wu ◽  
Guifeng Mu ◽  
Can Qin ◽  
Qiguang Miao ◽  
Wenping Ma ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Representation Learning ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Spatial Constraints ◽  
High Confidence ◽  
Great Ability ◽  
Classification Framework ◽  
Spatial Consistency

Because there are many unlabeled samples in hyperspectral images and the cost of manual labeling is high, this paper adopts semi-supervised learning method to make full use of many unlabeled samples. In addition, those hyperspectral images contain much spectral information and the convolutional neural networks have great ability in representation learning. This paper proposes a novel semi-supervised hyperspectral image classification framework which utilizes self-training to gradually assign highly confident pseudo labels to unlabeled samples by clustering and employs spatial constraints to regulate self-training process. Spatial constraints are introduced to exploit the spatial consistency within the image to correct and re-assign the mistakenly classified pseudo labels. Through the process of self-training, the sample points of high confidence are gradually increase, and they are added to the corresponding semantic classes, which makes semantic constraints gradually enhanced. At the same time, the increase in high confidence pseudo labels also contributes to regional consistency within hyperspectral images, which highlights the role of spatial constraints and improves the HSIc efficiency. Extensive experiments in HSIc demonstrate the effectiveness, robustness, and high accuracy of our approach.

A hyperspectral image classification framework and its application

2015 ◽  
Vol 299 ◽  
pp. 379-393 ◽  
Author(s):  
Shuiguang Deng ◽  
Yifei Xu ◽  
Yong He ◽  
Jianwei Yin ◽  
Zhaohui Wu
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Classification Framework

Hyperspectral Image Classification via Discriminative Sparse Representation with Extended LBP Texture

2014 ◽  
Vol 989-994 ◽  
pp. 3885-3888 ◽  
Author(s):  
Yue Mei Ren ◽  
Yan Ning Zhang ◽  
Wei Wei
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Optimization Problem ◽  
Hyperspectral Image ◽  
Local Binary Patterns ◽  
Hyperspectral Image Classification ◽  
Texture Information ◽  
Classification Framework ◽  
Representation Error

Hyperspectral images (HSI) have rich texture information, so combining texture information and image spectral information can improve the recognition accuracy. Sparse representation has significant success in image classification. In this paper, we propose a new discriminative sparse-based classification framework using spectral data and extended Local Binary Patterns (LBP) texture. Firstly, we propose an extended LBP coding for HSI classification. Then we formulate an optimization problem that combines the objective function of classification with the representation error by sparsity. Furthermore, we use a procedure similar to K-SVD algorithm to learn the discriminative dictionary. The experimental results show that the proposed discriminative spasity-based classification of image including the extended LBP texture outperforms the classical HSI classification algorithms.

A novel semi-supervised learning framework for hyperspectral image classification

2016 ◽  
Vol 14 (02) ◽  
pp. 1640005 ◽  
Author(s):  
Zhijing Ye ◽  
Hong Li ◽  
Yalong Song ◽  
Jianzhong Wang ◽  
Jon Atli Benediktsson
Keyword(s):  
Image Classification ◽  
Supervised Learning ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Hyperspectral Image Classification ◽  
Learning Framework ◽  
Classification Framework ◽  
Adaptive Interpolation

In this paper, we propose a novel semi-supervised learning classification framework using box-based smooth ordering and multiple 1D-embedding-based interpolation (M1DEI) in [J. Wang, Semi-supervised learning using multiple one-dimensional embedding-based adaptive interpolation, Int. J. Wavelets Multiresolut. Inf. Process. 14(2) (2016) 11 pp.] 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 classification is difficult, expensive and time-consuming, while unlabeled samples are easily available. The proposed method can effectively overcome the lack of labeled samples by introducing new labeled samples from unlabeled samples in a label boosting framework. Furthermore, the proposed method uses spatial information from the pixels in the neighborhood of the current pixel to better catch the features of hyperspectral image. The proposed idea is that, first, we extract the box (cube data) of each pixel from its neighborhood, then apply multiple 1D interpolation to construct the classifier. Experimental results on three hyperspectral data sets demonstrate that the proposed method is efficient, and outperforms recent popular semi-supervised methods in terms of accuracies.

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