Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning

Ma, Xiaorui; Wang, Hongyu; Wang, Jie

doi:10.1016/j.isprsjprs.2016.09.001

Cited by 103 publications

(34 citation statements)

References 37 publications

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“…A series of experiments are conducted to perform a comprehensive comparison with other state-of-the-art methods, including 2DBF [64], SVM [12], Laplacian SVM (LapSVM) [22,24] and CDL-MD-L [62]. 2DBF and 3DBF are feature extraction methods, SVM is a widely-used supervised classifier, while LapSVM, GANs and CDL-MD-L are classifiers based on semi-supervised learning.…”

Section: Methodsmentioning

confidence: 99%

“…4 spectral neighbors are adopted to calculate the Laplacian graph in the LapSVM. Three layers are used in the CDL-MD-L, whose window size and the number of hidden units are set to the same as [62]. The generator in the GANs has two hidden layers, and the number of units is set to 500 and 300, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The above-mentioned classification frameworks are supervised, which require a large number of labeled samples for training. Recently, a semi-supervised classifier based on multi-decision labeling and contextual deep learning (i.e., CDL-MD-L) is proposed by [62], which has demonstrated promising results in hyperspectral classification.…”

Section: Introductionmentioning

confidence: 99%

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Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

He¹,

Liu²,

Wang³

et al. 2017

Remote Sensing

138

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Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method, especially with a limited number of labeled samples.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

He¹,

Liu²,

Wang³

et al. 2017

Remote Sensing

138

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show abstract

“…It is critical to use spatial and spectral information for the effective increase of the detection performance in hyperspectral data [10]. Therefore, spatial information has been used for many algorithms, such as LSAD [11], DMSR [12], MD-L [13]. Generally, the method of combined spatial and spectral information has high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs

Zhao

Meng

et al. 2017

Sensors

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The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in available KRX detectors. Accordingly, this paper presents a novel weighted spatial-spectral kernel RX (WSSKRX) detector and its parallel implementation on graphics processing units (GPUs). The WSSKRX utilizes the spatial neighborhood resources to reconstruct the testing pixels by introducing a spectral factor and a spatial window, thereby effectively reducing the interference of background noise. Then, the kernel function is redesigned as a mapping trick in a KRX detector to implement the anomaly detection. In addition, a powerful architecture based on the GPU technique is designed to accelerate WSSKRX. To substantiate the performance of the proposed algorithm, both synthetic and real data are conducted for experiments.

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“…Deep learning brought about around 2006 [11], became well known in the computer vision community around 2012, since one supervised version of deep learning networks Deep Convolutional Neural Networks (DCNN) made a breakthrough for scene classification tasks [12,13], and has reached out to many industrial applications and other academic areas in recent years as it continues to advance technologies in areas, like speech recognition [14], medical diagnosis [15], autonomous driving [16], or even the gaming world [17,18]. When compared with other traditional classifiers, deep learning does not require feature engineering, which attracted many researchers from the remote sensing community to test its usability for landcover mapping [19][20][21][22][23]. Two latest review papers [20,24] on OBIA both also emphasize the need for testing deep learning techniques under the OBIA framework.Deep learning networks normally have a huge number of parameters to be adjusted during the training procedure and may require massive training samples to trigger its power, as shown in one of the latest studies [25], but collecting training samples is expensive for remote sensing applications.…”

mentioning

confidence: 99%

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

Liu

Abd-Elrahman

2018

Remote Sensing

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Fully Convolutional Networks (FCN) has shown better performance than other classifiers like Random Forest (RF), Support Vector Machine (SVM) and patch-based Deep Convolutional Neural Network (DCNN), for object-based classification using orthoimage only in previous studies; however, for further improving deep learning algorithm performance, multi-view data should be considered for training data enrichment, which has not been investigated for FCN. The present study developed a novel OBIA classification using FCN and multi-view data extracted from small Unmanned Aerial System (UAS) for mapping landcovers. Specifically, this study proposed three methods to automatically generate multi-view training samples from orthoimage training datasets to conduct multi-view object-based classification using FCN, and compared their performances with each other and also with RF, SVM, and DCNN classifiers. The first method does not consider the object surrounding information, while the other two utilized object context information. We demonstrated that all the three versions of FCN multi-view object-based classification outperformed their counterparts utilizing orthoimage data only. Furthermore, the results also showed that when multi-view training samples were prepared with consideration of object surroundings, FCN trained with these samples gave much better accuracy than FCN classification trained without context information. Similar accuracies were achieved from the two methods utilizing object surrounding information, although sample preparation was conducted using two different ways. When comparing FCN with RF, SVM, DCNN implies that FCN generally produced better accuracy than the other classifiers, regardless of using orthoimage or multi-view data.Remote Sens. 2018, 10, 457 2 of 24 all the UAS images. Then, image segmentation algorithm is conducted to segment the orthoimage to groups of homogeneous pixels to form numerous meaningful objects. Spectral, geometrical, textural, and contextual features are extracted from these objects and used as input to different classifiers, such as Random Forest (RF) [9] and Support Vector Machine (SVM) [10], to label the objects. Feature extraction and selection that have to be conducted during traditional OBIA procedures are challenging tasks and can limit classification performance.Recently, the rise of deep learning techniques provided an alternative to traditional land cover classifiers. Deep learning brought about around 2006 [11], became well known in the computer vision community around 2012, since one supervised version of deep learning networks Deep Convolutional Neural Networks (DCNN) made a breakthrough for scene classification tasks [12,13], and has reached out to many industrial applications and other academic areas in recent years as it continues to advance technologies in areas, like speech recognition [14], medical diagnosis [15], autonomous driving [16], or even the gaming world [17,18]. When compared with other traditional classifiers, deep learning does not require feature e...

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Semisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning

Cited by 103 publications

References 37 publications

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

A Weighted Spatial-Spectral Kernel RX Algorithm and Efficient Implementation on GPUs

An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System

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