Improved Active Deep Learning for Semi-Supervised Classification of Hyperspectral Image

Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully applied in semi-supervised classification tasks to address this problem, their performance inevitably meets the bottleneck due to the limitation of labeling cost. To address the aforementioned issue, this paper proposes a semi-supervised classification method for hyperspectral images that improves active deep learning. Specifically, the proposed model introduces the random multi-graph algorithm and replaces the expert mark in active learning with the anchor graph algorithm, which can label a considerable amount of unlabeled data precisely and automatically. In this way, a large number of pseudo-labeling samples would be added to the training subsets such that the model could be fine-tuned and the generalization performance could be improved without extra efforts for data manual labeling. Experiments based on three standard HSIs demonstrate that the proposed model can get better performance than other conventional methods, and they also outperform other studied algorithms in the case of a small training set.

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Overview of Hyperspectral Image Classification

Journal of Sensors ◽

10.1155/2020/4817234 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Wenjing Lv ◽

Xiaofei Wang

Keyword(s):

Supervised Classification ◽

Hyperspectral Image ◽

Unsupervised Classification ◽

Hyperspectral Images ◽

Hyperspectral Image Classification ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Good Classification ◽

Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

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Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419540272 ◽

2019 ◽

Vol 33 (09) ◽

pp. 1954027 ◽

Cited By ~ 1

Author(s):

Dexiang Zhang ◽

Jingzhong Kang ◽

Lina Xun ◽

Yu Huang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Spatial Information ◽

Hyperspectral Image ◽

Bilateral Filter ◽

Convolution Neural Network ◽

Hyperspectral Images ◽

Data Sets ◽

Edge Information

In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this paper also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available data sets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.

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Deep learning for supervised classification of temporal data in ecology

Ecological Informatics ◽

10.1016/j.ecoinf.2021.101252 ◽

2021 ◽

Vol 61 ◽

pp. 101252

Author(s):

César Capinha ◽

Ana Ceia-Hasse ◽

Andrew M. Kramer ◽

Christiaan Meijer

Keyword(s):

Deep Learning ◽

Supervised Classification ◽

Temporal Data

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UAV Image Multi-Labeling with Data-Efficient Transformers

Applied Sciences ◽

10.3390/app11093974 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3974

Author(s):

Laila Bashmal ◽

Yakoub Bazi ◽

Mohamad Mahmoud Al Rahhal ◽

Haikel Alhichri ◽

Naif Al Ajlan

Keyword(s):

Data Augmentation ◽

Feature Representation ◽

Aerial Image ◽

Remote Sensing Images ◽

Training Set ◽

Proposed Model ◽

Class Labels ◽

Using Data ◽

Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

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A Semi-supervised Classification Method for Hyperspectral Images by Triple Classifiers with Data Editing and Deep Learning

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications ◽

10.1007/978-3-030-48513-9_14 ◽

2020 ◽

pp. 171-183

Author(s):

Guoming Zhang ◽

Junshu Wang ◽

Ge Shi ◽

Jie Zhang ◽

Wanchun Dou

Keyword(s):

Deep Learning ◽

Supervised Classification ◽

Hyperspectral Images ◽

Classification Method ◽

Data Editing

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Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels

TecnoLógicas ◽

10.22430/22565337.1205 ◽

2019 ◽

Vol 22 (46) ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Jorge Luis Bacca ◽

Henry Arguello

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

Subspace Clustering ◽

Unsupervised Classification ◽

Hyperspectral Images ◽

Image Clustering ◽

Spectral Signature ◽

Spectral Image ◽

Sparse Subspace Clustering

Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces. Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

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Stock Pattern Classification from Charts using Deep Learning Algorithms

Academic Perspective Procedia ◽

10.33793/acperpro.03.01.89 ◽

2020 ◽

Vol 3 (1) ◽

pp. 445-454

Author(s):

Celal Buğra Kaya ◽

Alperen Yılmaz ◽

Gizem Nur Uzun ◽

Zeynep Hilal Kilimci

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Pattern Classification ◽

Short Term Memory ◽

Stock Exchange ◽

Learning Techniques ◽

Proposed Model ◽

Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

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