Cascade Convolutional Neural Network Based on Transfer-Learning for Aircraft Detection on High-Resolution Remote Sensing Images

Aircraft detection from high-resolution remote sensing images is important for civil and military applications. Recently, detection methods based on deep learning have rapidly advanced. However, they require numerous samples to train the detection model and cannot be directly used to efficiently handle large-area remote sensing images. A weakly supervised learning method (WSLM) can detect a target with few samples. However, it cannot extract an adequate number of features, and the detection accuracy requires improvement. We propose a cascade convolutional neural network (CCNN) framework based on transfer-learning and geometric feature constraints (GFC) for aircraft detection. It achieves high accuracy and efficient detection with relatively few samples. A high-accuracy detection model is first obtained using transfer-learning to fine-tune pretrained models with few samples. Then, a GFC region proposal filtering method improves detection efficiency. The CCNN framework completes the aircraft detection for large-area remote sensing images. The framework first-level network is an image classifier, which filters the entire image, excluding most areas with no aircraft. The second-level network is an object detector, which rapidly detects aircraft from the first-level network output. Compared with WSLM, detection accuracy increased by 3.66%, false detection decreased by 64%, and missed detection decreased by 23.1%.

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A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China

Remote Sensing ◽

10.3390/rs12142334 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2334

Author(s):

Lu Zhao ◽

Hongyan Ren ◽

Cheng Cui ◽

Yaohuan Huang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Uv Detection ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Detection Model ◽

Model Object ◽

Object Based ◽

Similar Area ◽

Urban Villages

High-resolution remotely sensed imageries have been widely employed to detect urban villages (UVs) in highly urbanized regions, especially in developing countries. However, the understanding of the potential impacts of spatially and temporally differentiated urban internal development on UV detection is still limited. In this study, a partition-strategy-based framework integrating the random forest (RF) model, object-based image analysis (OBIA) method, and high-resolution remote sensing images was proposed for the UV-detection model. In the core regions of Guangzhou, four original districts were re-divided into five new zones for the subsequent object-based RF-detection of UVs with a series features, according to the different proportion of construction lands. The results show that the proposed framework has a good performance on UV detection with an average overall accuracy of 90.23% and a kappa coefficient of 0.8. It also shows the possibility of transferring samples and models into a similar area. In summary, the partition strategy is a potential solution for the improvement of the UV-detection accuracy through high-resolution remote sensing images in Guangzhou. We suggest that the spatiotemporal process of urban construction land expansion should be comprehensively understood so as to ensure an efficient UV-detection in highly urbanized regions. This study can provide some meaningful clues for city managers identifying the UVs efficiently before devising and implementing their urban planning in the future.

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The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

The Journal of Supercomputing ◽

10.1007/s11227-020-03604-4 ◽

2021 ◽

Author(s):

Leijin Long ◽

Feng He ◽

Hongjiang Liu

Keyword(s):

Remote Sensing ◽

Southwest China ◽

Influence Factors ◽

Classification Error ◽

Model Parameters ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Jinsha River ◽

Detection Model ◽

High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

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Semantic Segmentation of Remote Sensing Images Using Transfer Learning and Deep Convolutional Neural Network With Dense Connection

IEEE Access ◽

10.1109/access.2020.3003914 ◽

2020 ◽

Vol 8 ◽

pp. 116744-116755 ◽

Cited By ~ 1

Author(s):

Binge Cui ◽

Xin Chen ◽

Yan Lu

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Semantic Segmentation ◽

Deep Convolutional Neural Network ◽

Remote Sensing Images

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A Single Target Grasp Detection Network Based on Convolutional Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/5512728 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Longzhi Zhang ◽

Dongmei Wu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

High Accuracy ◽

Experimental Results ◽

Detection Accuracy ◽

Single Object ◽

Single Target ◽

High Detection ◽

Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

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A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102086 ◽

2020 ◽

Vol 88 ◽

pp. 102086 ◽

Cited By ~ 8

Author(s):

Chenxiao Zhang ◽

Peng Yue ◽

Deodato Tapete ◽

Boyi Shangguan ◽

Mi Wang ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Convolutional Neural Network ◽

Land Cover Classification ◽

Remote Sensing Images ◽

Object Based ◽

Multi Level ◽

Very High

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A Neural Network-Based Distributed Compressed Sensing Algorithm to Derive Continuous Hyperspectral Images in Wireless Sensor Networks

10.21203/rs.3.rs-1234545/v1 ◽

2022 ◽

Author(s):

Md. Sarkar Hasanuzzaman

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Wireless Sensor Networks ◽

Sensor Networks ◽

High Resolution ◽

Super Resolution ◽

Hyperspectral Images ◽

Wireless Sensor ◽

Remote Sensing Images

Abstract Hyperspectral imaging is a versatile and powerful technology for gathering geo-data. Planes and satellites equipped with hyperspectral cameras are currently the leading contenders for large-scale imaging projects. Aiming at the shortcomings of traditional methods for detecting sparse representation of multi-spectral images, this paper proposes wireless sensor networks (WSNs) based single-hyperspectral image super-resolution method based on deep residual convolutional neural networks. We propose a different strategy that involves merging cheaper multispectral sensors to achieve hyperspectral-like spectral resolution while maintaining the WSN's spatial resolution. This method studies and mines the nonlinear relationship between low-resolution remote sensing images and high-resolution remote sensing images, constructs a deep residual convolutional neural network, connects multiple residual blocks in series, and removes some unnecessary modules. For this purpose, a decision support system is used that provides the outcome to the next layer. Finally, this paper, fully explores the similarities between natural images and hyperspectral images, use natural image samples to train convolutional neural networks, and further use migration learning to introduce the trained network model to the super-resolution problem of high-resolution remote sensing images, and solve the lack of training samples problem. A comparison between different algorithms for processing data on datasets collected in situ and via remote sensing is used to evaluate the proposed approach. The experimental results show that the method has good performance and can obtain better super-resolution effects.

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IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs11030286 ◽

2019 ◽

Vol 11 (3) ◽

pp. 286 ◽

Cited By ~ 24

Author(s):

Jiangqiao Yan ◽

Hongqi Wang ◽

Menglong Yan ◽

Wenhui Diao ◽

Xian Sun ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Object Detection ◽

Convolutional Neural Network ◽

State Of The Art ◽

Ground Truth ◽

Detection Performance ◽

Candidate Region ◽

Detection Accuracy ◽

Remote Sensing Images

Recently, methods based on Faster region-based convolutional neural network (R-CNN)have been popular in multi-class object detection in remote sensing images due to their outstandingdetection performance. The methods generally propose candidate region of interests (ROIs) througha region propose network (RPN), and the regions with high enough intersection-over-union (IoU)values against ground truth are treated as positive samples for training. In this paper, we find thatthe detection result of such methods is sensitive to the adaption of different IoU thresholds. Specially,detection performance of small objects is poor when choosing a normal higher threshold, while alower threshold will result in poor location accuracy caused by a large quantity of false positives.To address the above issues, we propose a novel IoU-Adaptive Deformable R-CNN framework formulti-class object detection. Specially, by analyzing the different roles that IoU can play in differentparts of the network, we propose an IoU-guided detection framework to reduce the loss of small objectinformation during training. Besides, the IoU-based weighted loss is designed, which can learn theIoU information of positive ROIs to improve the detection accuracy effectively. Finally, the class aspectratio constrained non-maximum suppression (CARC-NMS) is proposed, which further improves theprecision of the results. Extensive experiments validate the effectiveness of our approach and weachieve state-of-the-art detection performance on the DOTA dataset.

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