Convolutional Autoencoder-Based Multispectral Image Fusion

Azarang, Arian; Manoochehri, Hafez Eslami; Kehtarnavaz, Nasser

doi:10.1109/access.2019.2905511

Cited by 103 publications

(50 citation statements)

References 42 publications

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“…However, whether there were differences between each kind of behavior signal requires more study on characteristic distribution of each kind of sample signal generated by our network. The unidimensional Convolutional Auto-Encoder (CAE) can effectively show the deep characteristic differences between different samples [ 26 , 27 , 28 ]. Therefore, the output of the auto-encoder was set as a two-dimensional vector.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

Chen

Lei

2020

Sensors

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It is difficult to obtain many labeled Link Establishment (LE) behavior signals sent by non-cooperative short-wave radio stations. We propose a novel unidimensional Auxiliary Classifier Generative Adversarial Network (ACGAN) to get more signals and then use unidimensional DenseNet to recognize LE behaviors. Firstly, a few real samples were randomly selected from many real signals as the training set of unidimensional ACGAN. Then, the new training set was formed by combining real samples with fake samples generated by the trained ACGAN. In addition, the unidimensional convolutional auto-coder was proposed to describe the reliability of these generated samples. Finally, different LE behaviors could be recognized without the communication protocol standard by using the new training set to train unidimensional DenseNet. Experimental results revealed that unidimensional ACGAN effectively augmented the training set, thus improving the performance of recognition algorithm. When the number of original training samples was 400, 700, 1000, or 1300, the recognition accuracy of unidimensional ACGAN+DenseNet was 1.92, 6.16, 4.63, and 3.06% higher, respectively, than that of unidimensional DenseNet.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

Chen

Lei

2020

Sensors

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“…Meanwhile, several state-of-the-art algorithms, e.g. P + XS [25], HSI [6], PCA [7], guided image filtering (GIF) [26], convolutional neural network (CNN) [27], and convolutional autoencoder (CAE) [28] are used for qualitative and quantitative evaluation.…”

Section: Resultsmentioning

confidence: 99%

Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain

et al. 2020

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In this paper, a remote sensing image fusion method based on boundary measured dual-channel pulse-coupled neural network (PCNN) in multi-scale morphological gradient (MSMG) domain is proposed. Firstly, the panchromatic (PAN) image is decomposed into three parts, a small-scale image, a large-scale image, and a base image through a co-occurrence filtering (CoF)-based decomposition model. Secondly, an HSI transform is applied in the multispectral (MS) image to obtained intensity, hue and saturation components. Thirdly, a PCNN fusion strategy modulated by MSMG is used to fuse the base image and the intensity component of the MS image. Then, a fused intensity image is obtained by combining the small-scale image, large-scale image and the fused approximate image. Finally, the final fused image can be reconstructed by an inverse HSI transform. Experiments in four datasets demonstrate that the proposed method obtains the best performance in most cases. INDEX TERMS Remote sensing image fusion, co-occurrence filtering, multi-scale morphological gradient, pulse-coupled neural network.

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“…A recently approach using AE for pan-sharpening is proposed by Azarang et al [101] where the author consider a convolutional AE architecture, composed of an encoding and the associated decoding stage from improving the spatial information of the low-resolution MS bands. The objective is achieved by learning the nonlinear relationship between a PAN image and its spatially degraded version at a patch level, and uses the trained model to increase the spatial resolution of each MS band independently.…”

Section: Pan-sharpeningmentioning

confidence: 99%

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Tsagkatakis

Aidini

Fotiadou

et al. 2019

Sensors

116

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Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language understanding. In remote sensing, a large body of research has been devoted to the application of deep learning for typical supervised learning tasks such as classification. Less yet equally important effort has also been allocated to addressing the challenges associated with the enhancement of low-quality observations from remote sensing platforms. Addressing such channels is of paramount importance, both in itself, since high-altitude imaging, environmental conditions, and imaging systems trade-offs lead to low-quality observation, as well as to facilitate subsequent analysis, such as classification and detection. In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution, denoising, restoration, pan-sharpening, and fusion, among others. In addition to the detailed analysis and comparison of recently presented approaches, different research avenues which could be explored in the future are also discussed.

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Convolutional Autoencoder-Based Multispectral Image Fusion

Cited by 103 publications

References 42 publications

Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

Remote Sensing Image Fusion via Boundary Measured Dual-Channel PCNN in Multi-Scale Morphological Gradient Domain

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

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