An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)

Rahimzad, Maryam; Homayouni, Saeid; Naeini, Amin Alizadeh; Nadi, Saeed

doi:10.3390/rs13132501

Cited by 15 publications

(10 citation statements)

References 62 publications

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“…Achieving an optimal model requires tuning many inter-dependent parameters [28]. In this work, the optimal parameters for the learning process, especially the number of filters of encoder output, learning rate, and batch size were obtained via the Keras Tuner optimizer developed by the Google team [87].…”

Section: Experimental Results From the Proposed Cae Modelmentioning

confidence: 99%

“…Autoencoders (AEs) are one of the unsupervised architectures in deep neural networks and have been used in many applications in the field of RS. Therefore, some researchers have employed AEs to solve critical image processing challenges such as image classification [24][25][26], clustering [23,27,28], spectral unmixing [29] and image segmentation [30,31]. AEs have also been applied to deal with other important problems such as image fusion [32], change detection [33][34][35], pansharpening [36,37], anomaly detection [38,39], and image retrieval [40].…”

Section: Introductionmentioning

confidence: 99%

“…In [43], the authors introduced a 3D-CAE to encode embedded representations of the hyperspectral dataset, then applied clustering over them. Rahimzad et al [28] proposed a boosted CAE to learn and extract deep features for automatic RS image clustering in urban areas.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

et al. 2021

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This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled dataset for their training step. As an alternative, in this paper, we developed an unsupervised learning model by employing a convolutional auto-encoder (CAE) to deal with the problem of limited labeled data for training. The CAE was used to learn and extract the abstract and high-level features without using training data. To assess the performance of the proposed approach, we used Sentinel-2 imagery and a digital elevation model (DEM) to map landslides in three different case studies in India, China, and Taiwan. Using minimum noise fraction (MNF) transformation, we reduced the multispectral dimension to three features containing more than 80% of scene information. Next, these features were stacked with slope data and NDVI as inputs to the CAE model. The Huber reconstruction loss was used to evaluate the inputs. We achieved reconstruction losses ranging from 0.10 to 0.147 for the MNF features, slope, and NDVI stack for all three study areas. The mini-batch K-means clustering method was used to cluster the features into two to five classes. To evaluate the impact of deep features on landslide detection, we first clustered a stack of MNF features, slope, and NDVI, then the same ones plus with the deep features. For all cases, clustering based on deep features provided the highest precision, recall, F1-score, and mean intersection over the union in landslide detection.

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Section: Experimental Results From the Proposed Cae Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

et al. 2021

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“…Hence, the autoencoder is a data lossy compression algorithm. (3) Automatic learning means that the autoencoder automatically learns from data samples, making it easy to train a specific encoder to input a specified class without any new work (Rahimzad et al, 2021;Verma et al, 2021).…”

Section: Related Technologymentioning

confidence: 99%

The use of deep learning technology in dance movement generation

Liu

2022

Front. Neurorobot.

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The dance generated by the traditional music action matching and statistical mapping models is less consistent with the music itself. Moreover, new dance movements cannot be generated. A dance movement generation algorithm based on deep learning is designed to extract the mapping between sound and motion features to solve these problems. First, the sound and motion features are extracted from music and dance videos, and then, the model is built. In addition, a generator module, a discriminator module, and a self-encoder module are added to make the dance movement smoother and consistent with the music. The Pix2PixHD model is used to transform the dance pose sequence into a real version of the dance. Finally, the experiment takes the dance video on the network as the training data and trained 5,000 times. About 80% of the dance data are used as the training set and 20% as the test set. The experimental results show that Train, Valid, and Test values based on the Generator+Discriminator+Autoencoder model are 15.36, 17.19, and 19.12, respectively. The similarity between the generated dance sequence and the real dance sequence is 0.063, which shows that the proposed model can generate a dance more in line with the music. Moreover, the generated dance posture is closer to the real dance posture. The discussion has certain reference value for intelligent dance teaching, game field, cross-modal generation, and exploring the relationship between audio-visual information.

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“…Despite the paucity of relevant studies, researchers have demonstrated its effectiveness. Among them, the autoencoder is one of the unsupervised architectures embedded in DL models; it has been successfully used in the field of RS [52]. In the landslide detection task, Shahabi et al [53] first proposed an unsupervised model based on the convolutional auto−encoder (CAE), aiming to extract high−level features without using training data.…”

Section: Introductionmentioning

confidence: 99%

Efficient Detection of Earthquake−Triggered Landslides Based on U−Net++: An Example of the 2018 Hokkaido Eastern Iburi (Japan) Mw = 6.6 Earthquake

2022

Remote Sensing

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Efficient detection of earthquake−triggered landslides is crucial for emergency response and risk assessment. With the development of multi−source remote sensing images, artificial intelligence has gradually become a powerful landslide detection method for similar tasks, aiming to mitigate time−consuming problems and meet emergency requirements. In this study, a relatively new deep learning (DL) network, called U−Net++, was designed to detect landslides for regions affected by the Iburi, Japan Mw = 6.6 earthquake, with only small training samples. For feature extraction, ResNet50 was selected as the feature extraction layer, and transfer learning was adopted to introduce the pre−trained weights for accelerating the model convergence. To prove the feasibility and validity of the proposed model, the random forest algorithm (RF) was selected as the benchmark, and the F1−score, Kappa coefficient, and IoU (Intersection of Union) were chosen to quantitatively evaluate the model’s performance. In addition, the proposed model was trained with different sample sizes (256,512) and network depths (3,4,5), respectively, to analyze their impacts on performance. The results showed that both models detected the majority of landslides, while the proposed model obtained the highest metric value (F1−score = 0.7580, Kappa = 0.7441, and IoU = 0.6104) and was capable of resisting the noise. In addition, the proposed model trained with sample size 256 possessed optimal performance, proving that the size is a non−negligible parameter in U−Net++, and it was found that the U−Net++ trained with shallower layer 3 yielded better results than that with the standard layer 5. Finally, the outstanding performance of the proposed model on a public landslide dataset demonstrated the generalization of U−Net++.

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An Efficient Multi-Sensor Remote Sensing Image Clustering in Urban Areas via Boosted Convolutional Autoencoder (BCAE)

Cited by 15 publications

References 62 publications

Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

The use of deep learning technology in dance movement generation

Efficient Detection of Earthquake−Triggered Landslides Based on U−Net++: An Example of the 2018 Hokkaido Eastern Iburi (Japan) Mw = 6.6 Earthquake

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