Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

Han, Xian-Hua; Zhong, Yanfei; Cao, Liqin; Zhang, Liangpei

doi:10.3390/rs9080848

Cited by 285 publications

(153 citation statements)

References 39 publications

(39 reference statements)

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“…Deep learning and other machine learning algorithms are commonly used in remote sensing Lary et al, 2016;Han et al, 2017;Hu et al, 2015). Remote sensing is very suitable for machine learning because large datasets are available and the theoretical knowledge is incomplete (Lary et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing is very suitable for machine learning because large datasets are available and the theoretical knowledge is incomplete (Lary et al, 2016). For instance, Han et al (2017) introduced a modified pretrained AlexNet CNN and Hu et al (2015) used several CNNs (e.g., AlexNet and VGGnets) for remote sensing image classification. Ma et al (2015) used transfer learning in a SVM approach for classification of dust and clouds from satellite data.…”

Section: Introductionmentioning

confidence: 99%

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Identification of new particle formation events with deep learning

et al. 2018

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Abstract. New particle formation (NPF) in the atmosphere is globally an important source of climate relevant aerosol particles. Occurrence of NPF events is typically analyzed by researchers manually from particle size distribution data day by day, which is time consuming and the classification of event types may be inconsistent. To get more reliable and consistent results, the NPF event analysis should be automatized. We have developed an automatic analysis method based on deep learning, a subarea of machine learning, for NPF event identification. To our knowledge, this is the first time that a deep learning method, i.e., transfer learning of a convolutional neural network (CNN), has successfully been used to automatically classify NPF events into different classes directly from particle size distribution images, similarly to how the researchers carry out the manual classification. The developed method is based on image analysis of particle size distributions using a pretrained deep CNN, named AlexNet, which was transfer learned to recognize NPF event classes (six different types). In transfer learning, a partial set of particle size distribution images was used in the training stage of the CNN and the rest of the images for testing the success of the training. The method was utilized for a 15-yearlong dataset measured at San Pietro Capofiume (SPC) in Italy. We studied the performance of the training with different training and testing of image number ratios as well as with different regions of interest in the images. The results show that clear event (i.e., classes 1 and 2) and nonevent days can be identified with an accuracy of ca. 80 %, when the CNN classification is compared with that of an expert, which is a good first result for automatic NPF event analysis. In the event classification, the choice between different event classes is not an easy task even for trained researchers, and thus overlapping or confusion between different classes occurs. Hence, we cross-validated the learning results of CNN with the expert-made classification. The results show that the overlapping occurs, typically between the adjacent or similar type of classes, e.g., a manually classified Class 1 is categorized mainly into classes 1 and 2 by CNN, indicating that the manual and CNN classifications are very consistent for most of the days. The classification would be more consistent, by both human and CNN, if only two different classes are used for event days instead of three classes. Thus, we recommend that in the future analysis, event days should be categorized into classes of "quantifiable" (i.e., clear events, classes 1 and 2) and "nonquantifiable" (i.e., weak events, Class 3). This would better describe the difference of those classes: both formation and growth rates can be determined for quantifiable days but not both for nonquantifiable days. Furthermore, we investigated more deeply the days that are classified as clear events by experts and recognized as nonevents by the CNN and vice versa. Clear misclassifications seem to ...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of new particle formation events with deep learning

et al. 2018

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“…It is significant to automatically access the valuable information from the huge volume of the remote sensing data [1][2][3][4][5][6][7]. Objects in remote sensing images (RSIs) have many different orientations, size, and illumination densities since RSIs are taken from the upper airspace with different imaging conditions.…”

Section: Introductionmentioning

confidence: 99%

Airport Detection Using End-to-End Convolutional Neural Network with Hard Example Mining

et al. 2017

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Abstract:Deep convolutional neural network (CNN) achieves outstanding performance in the field of target detection. As one of the most typical targets in remote sensing images (RSIs), airport has attracted increasing attention in recent years. However, the essential challenge for using deep CNN to detect airport is the great imbalance between the number of airports and background examples in large-scale RSIs, which may lead to over-fitting. In this paper, we develop a hard example mining and weight-balanced strategy to construct a novel end-to-end convolutional neural network for airport detection. The initial motivation of the proposed method is that backgrounds contain an overwhelming number of easy examples and a few hard examples. Therefore, we design a hard example mining layer to automatically select hard examples by their losses, and implement a new weight-balanced loss function to optimize CNN. Meanwhile, the cascade design of proposal extraction and object detection in our network releases the constraint on input image size and reduces spurious false positives. Compared with geometric characteristics and low-level manually designed features, the hard example mining based network could extract high-level features, which is more robust for airport detection in complex environment. The proposed method is validated on a multi-scale dataset with complex background collected from Google Earth. The experimental results demonstrate that our proposed method is robust, and superior to the state-of-the-art airport detection models.

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“…Remote sensing is very suitable for machine learning because large datasets are available and the theoretical knowledge is incomplete (Lary et al, 2016). For instance, Han et al (2017) introduced a modified pre-trained AlexNet CNN and Hu et al (2015) used several CNNs (e.g. AlexNet and VGGnets) for remote sensing image 15 classification.…”

mentioning

confidence: 99%

“…Deep learning and other machine learning algorithms are commonly used in remote sensing (Zhang et al, 2016;Lary et al, 2016;Han et al, 2017;Hu et al, 2015). Remote sensing is very suitable for machine learning because large datasets are available and the theoretical knowledge is incomplete (Lary et al, 2016).…”

mentioning

confidence: 99%

Identification of new particle formation events with deep learning

Joutsensaari¹,

Ozon²,

Nieminen³

et al. 2018

Preprint

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Abstract. New particle formation (NPF) in the atmosphere is globally an important source of climate relevant aerosol particles.Occurrence of NPF events is typically analyzed manually by researchers from particle size distribution data day by day, which 10 is time consuming and the classification of event types may be inconsistent. To get more reliable and consistent results, the NPF event analysis should be automatized. We have developed an automatic analysis method based on deep learning, a subarea of machine learning, for NPF event identification. To our knowledge, this is the first time when NPF events have been successfully classified automatically into different classes from particle size distribution images. The developed method is based on image analysis of particle size distributions using a pre-trained deep Convolutional Neural Networks (CNN), named 15 AlexNet, which was transfer learned to recognize NPF event classes (six different types). In transfer learning, a partial set of particle size distribution images were used in the training stage of the CNN and the rest of images for testing the success of the training. The method was utilized for a 15-year long dataset measured at San Pietro Capofiume in Italy. We studied performance of the training with different training and testing image number ratios as well as with different regions of interest in the images. The results show that clear event (i.e., Classes 1 and 2) and non-event days can be identified with an accuracy 20 of ca. 80 %, when the CNN classification is compared with that of an expert, which is a good first result for automatic NPF event analysis. In the event classification, the choice between different event classes is not an easy task even for trained researchers, thus overlapping or confusion between different classes occurs. Hence, we cross validated the learning results of CNN with the expert made classification. The results show that the overlapping occurs typically between the adjacent or similar type of classes, e.g., a manually classified Class 1 is categorized mainly into Classes 1 and 2 by CNN, indicating that the 25 manual and CNN classifications are very consist for the most of the days. The classification would be more consistent, by both human and CNN, if only two different classes are used for event days instead of three classes. Thus, we recommend that in the future analysis, event days should be categorized into classes of "Quantifiable" (i.e. clear events, Classes 1 and 2) and "NonQuantifiable" (i.e. weak events, Class 3). This would better describe the difference of those classes: both formation and growth rates can be determined for Quantifiable days but not both for Non-Quantifiable days. Furthermore, we investigated more 30 deeply the days that are classified as clear events by experts and recognized as non-events by the CNN and vice versa. Clear misclassifications seem to occur more commonly in manual analysis than in the CNN categorization, which is mostly due to Atmos. Chem. Phys. Discuss., https://doi.org/10.51...

show abstract

Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

Cited by 285 publications

References 39 publications

Identification of new particle formation events with deep learning

Identification of new particle formation events with deep learning

Airport Detection Using End-to-End Convolutional Neural Network with Hard Example Mining

Identification of new particle formation events with deep learning

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