GAN-NL: Unsupervised Representation Learning for Remote Sensing Image Classification

Duan, Yiping; Tao, Xiaoming; Xu, Mai; Han, Chaoyi; Lu, Jianhua

doi:10.1109/globalsip.2018.8646414

Cited by 12 publications

(10 citation statements)

References 11 publications

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“…Fully supervised learning can hardly work well without a large data set with clean labels. As a promising unsupervised learning method, generative adversarial networks have been used for tackling scene classification with data sets that lack annotations [83], [84], [137]. Consequently, it is valuable to explore unsupervised learning for scene classification.…”

Section: Future Opportunitiesmentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Cheng

Xie

Han

et al. 2020

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

620

267

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Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs. However, to the best of our knowledge, a comprehensive review of recent achievements regarding deep learning for scene classification of remote sensing images is still lacking. Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers. To be specific, we discuss the main challenges of remote sensing image scene classification and survey (1) Autoencoder-based remote sensing image scene classification methods, (2) Convolutional Neural Network-based remote sensing image scene classification methods, and (3) Generative Adversarial Network-based remote sensing image scene classification methods. In addition, we introduce the benchmarks used for remote sensing image scene classification and summarize the performance of more than two dozen of representative algorithms on three commonly-used benchmark data sets. Finally, we discuss the promising opportunities for further research.

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Section: Future Opportunitiesmentioning

confidence: 99%

“…Data from google scholar advanced search: allintitle: ("remote sensing" or "aerial" or "satellite" or "land use") and "scene classification". been employed by some researchers on the field of remote sensing image scene classification [83], [84].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Cheng

Xie

Han

et al. 2020

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

620

267

View full text Add to dashboard Cite

show abstract

“…Since the advent of CNNs, many modules and training methods have been proposed to improve the adaptability of neural networks in remote sensing [13,14]. Many models provide us with ideas on how to solve the problem of remote sensing image scene classification under the small sample conditions [15,16].…”

Section: Introductionmentioning

confidence: 99%

Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

Zhang

Luo

et al. 2021

Remote Sensing

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Deep learning is an important research method in the remote sensing field. However, samples of remote sensing images are relatively few in real life, and those with markers are scarce. Many neural networks represented by Generative Adversarial Networks (GANs) can learn from real samples to generate pseudosamples, rather than traditional methods that often require more time and man-power to obtain samples. However, the generated pseudosamples often have poor realism and cannot be reliably used as the basis for various analyses and applications in the field of remote sensing. To address the abovementioned problems, a pseudolabeled sample generation method is proposed in this work and applied to scene classification of remote sensing images. The improved unconditional generative model that can be learned from a single natural image (Improved SinGAN) with an attention mechanism can effectively generate enough pseudolabeled samples from a single remote sensing scene image sample. Pseudosamples generated by the improved SinGAN model have stronger realism and relatively less training time, and the extracted features are easily recognized in the classification network. The improved SinGAN can better identify sub-jects from images with complex ground scenes compared with the original network. This mechanism solves the problem of geographic errors of generated pseudosamples. This study incorporated the generated pseudosamples into training data for the classification experiment. The result showed that the SinGAN model with the integration of the attention mechanism can better guarantee feature extraction of the training data. Thus, the quality of the generated samples is improved and the classification accuracy and stability of the classification network are also enhanced.

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“…To mitigate the problem of overfitting that generally arises due to the limited availability of annotated data, generative adversarial networks (GANs) became popular to address scene classification problems in RS. GANs learn the hidden structure in the given input data and consists of a generator (that learns the semantic contents of the data) and a discriminator (that classifies the generated and input images) [24][25][26][27][28][29]. MARTA-GAN proposed in [27], was one among the first efforts to learn feature representations to perform unsupervised aerial scene classification.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of GAN models depends on the quality of the modelling of the structure of generated images. In [28], GAN-NL has proposed to effectively model the non-local dependencies in the generated images and has shown remarkable improvements in the classification accuracies. Roy et al [26] proposed a semantic fusion GAN, where the feature representations are obtained using a standard Deep Convolutional GAN (DCGAN) combined with an external deep network.…”

Section: Introductionmentioning

confidence: 99%

Remote-Sensing Image Scene Classification With Deep Neural Networks in JPEG 2000 Compressed Domain

Byju

Sümbül

Demir

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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To reduce the storage requirements, remote sensing (RS) images are usually stored in compressed format. Existing scene classification approaches using deep neural networks (DNNs) require to fully decompress the images, which is a computationally demanding task in operational applications. To address this issue, in this paper we propose a novel approach to achieve scene classification in JPEG 2000 compressed RS images. The proposed approach consists of two main steps: i) approximation of the finer resolution sub-bands of reversible biorthogonal wavelet filters used in JPEG 2000; and ii) characterization of the high-level semantic content of approximated wavelet sub-bands and scene classification based on the learnt descriptors. This is achieved by taking codestreams associated with the coarsest resolution wavelet sub-band as input to approximate finer resolution sub-bands using a number of transposed convolutional layers. Then, a series of convolutional layers models the high-level semantic content of the approximated wavelet subband. Thus, the proposed approach models the multiresolution paradigm given in the JPEG 2000 compression algorithm in an end-to-end trainable unified neural network. In the classification stage, the proposed approach takes only the coarsest resolution wavelet sub-bands as input, thereby reducing the time required to apply decoding. Experimental results performed on two benchmark aerial image archives demonstrate that the proposed approach significantly reduces the computational time with similar classification accuracies when compared to traditional RS scene classification approaches (which requires full image decompression).

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GAN-NL: Unsupervised Representation Learning for Remote Sensing Image Classification

Cited by 12 publications

References 11 publications

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

Improved SinGAN Integrated with an Attentional Mechanism for Remote Sensing Image Classification

Remote-Sensing Image Scene Classification With Deep Neural Networks in JPEG 2000 Compressed Domain

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