Active learning based autoencoder for hyperspectral imagery classification

Sun, Yibao; Zhang, Liangpei; Wang, Wei; Chen, Zeqiang

doi:10.1109/igarss.2016.7729116

Cited by 20 publications

(10 citation statements)

References 13 publications

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“…In the HSIC literature, several works have combined the AL and DNN. For instance, [310] joined autoencoder with AL technique and [311] proposed a DBN-based AL framework for HSIC. Similarly, [312] coupled Bayesian CNN with AL paradigm for spectral-spatial HSIC.…”

Section: Fig 14: a General Overview Of Active Learningmentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

222

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Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the stateof-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that

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Section: Fig 14: a General Overview Of Active Learningmentioning

confidence: 99%

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Ahmad

Shabbir

Roy³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

222

View full text Add to dashboard Cite

show abstract

“…In contrast, the active learning method based on posterior probability [98,99,100] is more widely used. Breaking ties belongs to the active learning method of posterior probability, which is widely used in hyperspectral classification tasks.…”

Section: Deep Active Learning For Hsi Classificationmentioning

confidence: 99%

“…[98], Li et al first used an autoencoder to construct an active learning model for hyperspectral image classification tasks. At the same time, Sun et al[99] also…”

mentioning

confidence: 93%

A survey: Deep learning for hyperspectral image classification with few labeled samples

et al. 2021

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With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-theart approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.

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“…A common thread in these works is the notion that choosing samples that confuse the machine the most would result in a better (efficient) active learning performance. Active learning with deep neural networks has obtained increasing attention within the remote sensing community in recent years [54,55,56,57,58]. Liu et al [55] used features produced by a DBN to estimate the representativeness and uncertainty of samples.…”

Section: Active Learningmentioning

confidence: 99%

Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios

Zhou

Prasad

2020

Hyperspectral Image Analysis

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Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation-these are primarily applied to color imagery and video. In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks. These multichannel images come with their own unique set of challenges that must be addressed for effective image analysis. Challenges include limited ground truth (annotation is expensive and extensive labeling is often not feasible), and high dimensional nature of the data (each pixel is represented by hundreds of spectral bands), despite being presented by a large amount of unlabeled data and the potential to leverage multiple sensors/sources that observe the same scene. In this chapter, we will review recent advances in the community that leverage deep learning for robust hyperspectral image analysis despite these unique challenges-specifically, we will review unsupervised, semi-supervised and active learning approaches to image analysis, as well as transfer learning approaches for multi-source (e.g. multi-sensor, or multi-temporal) image analysis.

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Active learning based autoencoder for hyperspectral imagery classification

Cited by 20 publications

References 13 publications

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects

A survey: Deep learning for hyperspectral image classification with few labeled samples

Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios

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