GCSANetA Global Context Spatial Attention Deep Learning Network for Remote Sensing Scene Classification

Ship detection is a significant and challenging task in remote sensing. At present, due to the faster speed and higher accuracy, the deep learning method has been widely applied in the field of ship detection. In ship detection, targets usually have the characteristics of arbitrary-oriented property and large aspect ratio. In order to take full advantage of these features to improve speed and accuracy on the base of deep learning methods, this article proposes an anchor-free method, which is referred as CPS-Det, on ship detection using rotatable bounding box. The main improvements of CPS-Det as well as the contributions of this article are as follows. First, an anchor-free based deep learning network was used to improve speed with fewer parameters. Second, an annotation method of oblique rectangular frame is proposed, which solves the problem that periodic angle and bounded coordinates in conjunction with the regression calculation can lead to the problem of loss anomalies. For the annotation scheme proposed in this paper, a scheme for calculating Angle Loss is proposed, which makes the loss function of angle near the boundary value more accurate and greatly improves the accuracy of angle prediction. Third, the centerness calculation of feature points is optimized in this article so that the center weight distribution of each point is suitable for the rotation detection. Finally, a scheme combining centerness and positive sample screening is proposed and its effectiveness in ship detection is proved. Experiments on remote sensing public dataset HRSC2016 show the effectiveness of our approach.

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Land Cover Classification From VHR Optical Remote Sensing Images by Feature Ensemble Deep Learning Network

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2019.2947022 ◽

2020 ◽

Vol 17 (8) ◽

pp. 1396-1400 ◽

Cited By ~ 3

Author(s):

Shan Dong ◽

Yin Zhuang ◽

Zhanxin Yang ◽

Long Pang ◽

He Chen ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Land Cover ◽

Land Cover Classification ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Learning Network ◽

Deep Learning Network

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Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification

Remote Sensing ◽

10.3390/rs13091689 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1689

Author(s):

Chuang Lin ◽

Shanxin Guo ◽

Jinsong Chen ◽

Luyi Sun ◽

Xiaorou Zheng ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Network Performance ◽

Aerial Image ◽

Label Noise ◽

Remote Sensing Classification ◽

Learning Network ◽

Training Samples ◽

Deep Learning Network ◽

The Impact

The deep-learning-network performance depends on the accuracy of the training samples. The training samples are commonly labeled by human visual investigation or inherited from historical land-cover or land-use maps, which usually contain label noise, depending on subjective knowledge and the time of the historical map. Helping the network to distinguish noisy labels during the training process is a prerequisite for applying the model for training across time and locations. This study proposes an antinoise framework, the Weight Loss Network (WLN), to achieve this goal. The WLN contains three main parts: (1) the segmentation subnetwork, which any state-of-the-art segmentation network can replace; (2) the attention subnetwork (λ); and (3) the class-balance coefficient (α). Four types of label noise (an insufficient label, redundant label, missing label and incorrect label) were simulated by dilate and erode processing to test the network’s antinoise ability. The segmentation task was set to extract buildings from the Inria Aerial Image Labeling Dataset, which includes Austin, Chicago, Kitsap County, Western Tyrol and Vienna. The network’s performance was evaluated by comparing it with the original U-Net model by adding noisy training samples with different noise rates and noise levels. The result shows that the proposed antinoise framework (WLN) can maintain high accuracy, while the accuracy of the U-Net model dropped. Specifically, after adding 50% of dilated-label samples at noise level 3, the U-Net model’s accuracy dropped by 12.7% for OA, 20.7% for the Mean Intersection over Union (MIOU) and 13.8% for Kappa scores. By contrast, the accuracy of the WLN dropped by 0.2% for OA, 0.3% for the MIOU and 0.8% for Kappa scores. For eroded-label samples at the same level, the accuracy of the U-Net model dropped by 8.4% for OA, 24.2% for the MIOU and 43.3% for Kappa scores, while the accuracy of the WLN dropped by 4.5% for OA, 4.7% for the MIOU and 0.5% for Kappa scores. This result shows that the antinoise framework proposed in this paper can help current segmentation models to avoid the impact of noisy training labels and has the potential to be trained by a larger remote sensing image set regardless of the inner label error.

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Research on Remote Sensing Image Data Attack Method Based on Machine Deep Learning Network

Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence ◽

10.1145/3377713.3377787 ◽

2019 ◽

Author(s):

Song Yuchen ◽

Tang Dejin ◽

Zhou Xiaoming ◽

Song Yuanchen

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Image Data ◽

Remote Sensing Image ◽

Learning Network ◽

Deep Learning Network

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Transferability of Convolutional Neural Network Models for Identifying Damaged Buildings Due to Earthquake

Remote Sensing ◽

10.3390/rs13030504 ◽

2021 ◽

Vol 13 (3) ◽

pp. 504

Author(s):

Wanting Yang ◽

Xianfeng Zhang ◽

Peng Luo

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Model ◽

Remote Sensing Data ◽

Remote Sensing Images ◽

Learning Network ◽

Building Information ◽

Deep Learning Network

The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted four Convolutional Neural Network (CNN) models for extracting damaged building information and compared their performance. A sample dataset of damaged buildings was constructed by using multiple disaster images retrieved from the xBD dataset. Using satellite and aerial remote sensing data obtained after the 2008 Wenchuan earthquake, we examined the geographic and data transferability of the deep network model pre-trained on the xBD dataset. The result shows that the network model pre-trained with samples generated from multiple disaster remote sensing images can extract accurately collapsed building information from satellite remote sensing data. Among the adjusted CNN models tested in the study, the adjusted DenseNet121 was the most robust. Transfer learning solved the problem of poor adaptability of the network model to remote sensing images acquired by different platforms and could identify disaster-damaged buildings properly. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.

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