SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search

Ma, Ailong; Wan, Yat-wah; Zhong, Yanfei; Wang, Junjue; Zhang, Liangpei

doi:10.1016/j.isprsjprs.2020.11.025

Cited by 106 publications

(38 citation statements)

References 51 publications

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“…Recently, neuroevolution algorithms have been used in many machine learning tasks to improve the accuracy of deep learning models [13]. In [14], neuroevolution search was used to evolve neural networks for object classification in high-resolution remote sensing images. In [15], the authors presented a neuroevolution algorithm for standard image classification.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Faber

Pietroń

Żurek

2021

Entropy

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Multivariate time series anomaly detection is a widespread problem in the field of failure prevention. Fast prevention means lower repair costs and losses. The amount of sensors in novel industry systems makes the anomaly detection process quite difficult for humans. Algorithms that automate the process of detecting anomalies are crucial in modern failure prevention systems. Therefore, many machine learning models have been designed to address this problem. Mostly, they are autoencoder-based architectures with some generative adversarial elements. This work shows a framework that incorporates neuroevolution methods to boost the anomaly detection scores of new and already known models. The presented approach adapts evolution strategies for evolving an ensemble model, in which every single model works on a subgroup of data sensors. The next goal of neuroevolution is to optimize the architecture and hyperparameters such as the window size, the number of layers, and the layer depths. The proposed framework shows that it is possible to boost most anomaly detection deep learning models in a reasonable time and a fully automated mode. We ran tests on the SWAT and WADI datasets. To the best of our knowledge, this is the first approach in which an ensemble deep learning anomaly detection model is built in a fully automatic way using a neuroevolution strategy.

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Section: Related Workmentioning

confidence: 99%

Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Faber

Pietroń

Żurek

2021

Entropy

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“…conduct the training on a large quantity of labeled data to extract deep image features [8], [18], [24]. Among them, the convolutional neural networks (CNN) are widely used; they can be divided into three categories, depending on whether the CNN is trained and how it is used: 1) New deep network trained from scratch.…”

Section: Introductionmentioning

confidence: 99%

GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification

Chen

Ouyang

Wei

et al. 2022

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

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Deep convolutional neural networks have become an indispensable method in remote sensing image scene classification because of their powerful feature extraction capabilities. However, the ability of the models to extract multi-scale features and global features on surface objects of complex scenes is currently insufficient. We propose a framework based on global context spatial attention (GCSA) and densely connected convolutional networks to extract multi-scale global scene features, called GCSANet. The mixup operation is used to enhance the spatial mixed data of remote sensing images, and the discrete sample space is rendered continuous to improve the smoothness in the neighborhood of the data space. The characteristics of multi-scale surface objects are extracted, and their internal dense connection is strengthened by the densely connected backbone network. GCSA is introduced into the densely connected backbone network to encode the context information of the remote sensing scene image into the local features. Experiments were performed on four remote sensing scene datasets to evaluate the performance of GCSANet. The GCSANet achieved the highest classification precision on AID and NWPU datasets and the second best performance on the UCM dataset, indicating the GCSANet can effectively extract the global features of remote sensing images. In addition, the GCSANet presents the highest classification accuracy on the constructed mountain image scene dataset. These results reveal that the GCSANet can effectively extract multi-scale global scene features on complex remote sensing scenes. The source codes of this method can be found in https://github.com/ShubingOuyangcug/GCSANet.

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“…With the development of computer vision technology based on deep learning, the convolutional neural networks (CNNs) are gradually applied to remote sensing image classification [9][10][11] or ground object detection [12,13]. Long et al [14] first designed the fully convolutional network (FCN) in 2015, using the convolution layer to replace the fully connected layer of the CNNs.…”

Section: Introductionmentioning

confidence: 99%

Building Multi-Feature Fusion Refined Network for Building Extraction from High-Resolution Remote Sensing Images

et al. 2021

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Deep learning approaches have been widely used in building automatic extraction tasks and have made great progress in recent years. However, the missing detection and wrong detection causing by spectrum confusion is still a great challenge. The existing fully convolutional networks (FCNs) cannot effectively distinguish whether the feature differences are from one building or the building and its adjacent non-building objects. In order to overcome the limitations, a building multi-feature fusion refined network (BMFR-Net) was presented in this paper to extract buildings accurately and completely. BMFR-Net is based on an encoding and decoding structure, mainly consisting of two parts: the continuous atrous convolution pyramid (CACP) module and the multiscale output fusion constraint (MOFC) structure. The CACP module is positioned at the end of the contracting path and it effectively minimizes the loss of effective information in multiscale feature extraction and fusion by using parallel continuous small-scale atrous convolution. To improve the ability to aggregate semantic information from the context, the MOFC structure performs predictive output at each stage of the expanding path and integrates the results into the network. Furthermore, the multilevel joint weighted loss function effectively updates parameters well away from the output layer, enhancing the learning capacity of the network for low-level abstract features. The experimental results demonstrate that the proposed BMFR-Net outperforms the other five state-of-the-art approaches in both visual interpretation and quantitative evaluation.

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SceneNet: Remote sensing scene classification deep learning network using multi-objective neural evolution architecture search

Cited by 106 publications

References 51 publications

Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

GCSANet: A Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification

Building Multi-Feature Fusion Refined Network for Building Extraction from High-Resolution Remote Sensing Images

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