Active-Learning-Incorporated Deep Transfer Learning for Hyperspectral Image Classification

Lin, Jianzhe; Zhao, Liang; Li, Shuying; Ward, Rabab K.; Wang, Z. Jane

doi:10.1109/jstars.2018.2874225

Cited by 54 publications

(36 citation statements)

References 31 publications

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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%

“…Liu et al [55] used features produced by a DBN to estimate the representativeness and uncertainty of samples. Both [56] and [57] explored using an active learning strategy to facilitate transferring knowledge from one dataset to another. In [56], a stacked sparse autoencoder was initially trained in the source domain and then fine-tuned in the target domain.…”

Section: Active Learningmentioning

confidence: 99%

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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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Section: Active Learningmentioning

confidence: 99%

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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“…In the first experiment, we compare the proposed fusion method with three classical hyperspectral classification methods, i.e., the SVM [5] and the SAM [39], and the k-NN method, respectively. Moreover, a recently developed method, i.e., the transfer learning (TL) based Convolutional Neural Network [46] is also used to assess the performance of the proposed method against the state-of-the-art approaches [6,7]. For the TL method, the network we used is a Convolutional Neural Network (CNN) VGGNet-VD16 (see website [47]), which is already trained by a visual dataset ImageNet.…”

Section: Classifiers Combination Correlation Q-statisticmentioning

confidence: 99%

“…In recent years, the hyperspectral image classification has been applied to many applications [3]. Typical techniques applied to hyperspectral image classification include many traditional pattern recognition methods [4], kernel based methods [5], and recently developed deep learning approaches, such as the transfer learning [6] and the active learning [7], etc. Data fusion involves the combination of information from different sources, either with differing textual or rich-media representations.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification

Guo

2019

Remote Sensing

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To better classify remotely sensed hyperspectral imagery, we study hyperspectral signatures from a different view, in which the discriminatory information is divided as reflectance features and absorption features, respectively. Based on this categorization, we put forward an information fusion approach, where the reflectance features and the absorption features are processed by different algorithms. Their outputs are considered as initial decisions, and then fused by a decision-level algorithm, where the entropy of the classification output is used to balance between the two decisions. The final decision is reached by modifying the decision of the reflectance features via the results of the absorption features. Simulations are carried out to assess the classification performance based on two AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) hyperspectral datasets. The results show that the proposed method increases the classification accuracy against the state-of-the-art methods.Extended from our early research on acoustic signals fusion [19] and hyperspectral image classification [20,21], in this research we focus on decision fusion approaches from a broader choice of fusion collections. Decision level fusion can be viewed as a procedure of choosing one hypothesis from multiple hypotheses given multiple sources. Generally speaking, decision level fusion is used to improve decision accuracy as well as reduce communication burden. Here, for the purpose of better scene classification accuracy, we adopt the decision level fusion to obtain a new decision from one single hyperspectral data source. One particular reason for such a choice is that in the decision level fusion the fused information may or may not come from the identical sensors. In the first manner, the decision fusion can combine the outputs of each classifier to make an overall decision. On the contrary, the data-level and the feature-level fusion usually integrate multiple data source or multiple feature sets. Thus, information fusion can be implemented, either by combining different sensors' output (like the traditional fusion), or by integrating different knowledge extractions (such as "experts' different views"). The latter fusion scheme actually compensates the deficiency inherited from a single view or a single knowledge description. Thus, on a typical hyperspectral data-cube that is apparently acquired from a single hyperspectral sensor, we are still able to explore many effective decision fusion strategies. By this idea, we recover new opportunities to further improve the classification performance, especially for the high-dimensional remotely sensed data such as the hyperspectral imagery that is rich in feature representation and interpretation.Following the aforementioned motivation, we propose a novel decision level fusion framework for hyperspectral image classification. In the first step, we extract the spectra from every pixel, and use them as holistic features. These features characterize the lighting interaction between the materi...

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“…Recent studies have been carried out on AL-SSL methods in remote sensing communities [12][13][14][15], and they have achieved notable improvements in the learning performance. Various strategies combine AL and SSL [16]. In this paper, we used an encapsulated way, which uses merely SSL as the classifier model of the AL.…”

Section: Introductionmentioning

confidence: 99%

A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification

2020

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This paper introduces a novel multi-view multi-learner (MVML) active learning method, in which the different views are generated by a genetic algorithm (GA). The GA-based view generation method attempts to construct diverse, sufficient, and independent views by considering both inter- and intra-view confidences. Hyperspectral data inherently owns high dimensionality, which makes it suitable for multi-view learning algorithms. Furthermore, by employing multiple learners at each view, a more accurate estimation of the underlying data distribution can be obtained. We also implemented a spectral-spatial graph-based semi-supervised learning (SSL) method as the classifier, which improved the performance of the classification task in comparison with supervised learning. The evaluation of the proposed method was based on three different benchmark hyperspectral data sets. The results were also compared with other state-of-the-art AL-SSL methods. The experimental results demonstrated the efficiency and statistically significant superiority of the proposed method. The GA-MVML AL method improved the classification performances by 16.68%, 18.37%, and 15.1% for different data sets after 40 iterations.

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Active-Learning-Incorporated Deep Transfer Learning for Hyperspectral Image Classification

Cited by 54 publications

References 31 publications

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

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

Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification

A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification

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