A Novel Region-Based Image Registration Method for Multisource Remote Sensing Images Via CNN

Zeng, Lijiang; Du, Yan; Lin, Hung‐Mo; Wang, Jing; Yin, Junjun; Yang, Jian

doi:10.1109/jstars.2020.3047656

Cited by 15 publications

(8 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This calls for more powerful model resulting in higher demand on dataset. The late-fusion methods, such as concatenating the semantic features from near infrared, red, green (IRRG) spectrum and Digital Surface Model (DSM) data in channel dimension [5], concatenating optical image features and DSM data features in [29], concatenating two regional feature maps from optical image and Synthetic Aperture Radar data in [30], and fusing features from HRSI and DEM data by adding pixels with the same positions in spatial dimension [31], effectively fuse the essential semantic information requried for the task from heterogeneous features.…”

Section: Related Workmentioning

confidence: 99%

Feature-Fusion Segmentation Network for Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

Liu

Peng

et al. 2023

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

As a harzard disaster, landslide often brings tremendous losses to humanity, so it's necessary to achieve reliable detection of landslide. However, the problems of visual blur and small-sized dataset cause great challenges for old landslide detection task when using remote sensing data. To reliably extract semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from the boundaries of landslides through HPCL and fuses the heterogeneous infromation in the semantic space from High-Resolution Remote Sensing Images and Digital Elevation Model Data data. For full utilization of the precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method, which includes the construction of global queues that store hyperpixel-wise samples and the updating scheme of a momentum encoder, is developed, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide dataset and experiment results show that the model greatly improves the reliablity of old landslide detection compared to the previous old landslide segmentation model, where mIoU metric is increased from 0.620 to 0.651, Landslide IoU metric is increased from 0.334 to 0.394 and F1score metric is increased from 0.501 to 0.565.

show abstract

Section: Related Workmentioning

confidence: 99%

Feature-Fusion Segmentation Network for Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

Liu

Peng

et al. 2023

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Similar to D2Net, first, images I 1 and I 2 are given, and a pair of the corresponding feature points A and B are in I 1 and I 2 , respectively, where A ∈ I 1 and B ∈ I 2 . The distance between the soft descriptors of A and B is derived from Equation (10).…”

Section: Training Lossmentioning

confidence: 99%

“…Therefore, it is difficult to achieve reliable matching with multiview heterogeneous images by using only traditional artificial image-gradient-based operators (such as the scale-invariant feature transform (SIFT)) [8]. With the development of deep learning, convolutional neural networks (CNNs) have achieved great success in the field of image processing [9][10][11]. The convolutional layer in a CNN has strong feature extraction ability.…”

Section: Introductionmentioning

confidence: 99%

Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network

Liu

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Matching aerial and satellite optical images with large dip angles is a core technology and is essential for target positioning and dynamic monitoring in sensitive areas. However, due to the long distances and large dip angle observations of the aerial platform, there are significant perspective, radiation, and scale differences between heterologous space-sky images, which seriously affect the accuracy and robustness of feature matching. In this paper, a multiview satellite and unmanned aerial vehicle (UAV) image matching method based on deep learning is proposed to solve this problem. The main innovation of this approach is to propose a joint descriptor consisting of soft descriptions and hard descriptions. Hard descriptions are used as the main description to ensure matching accuracy. Soft descriptions are used not only as auxiliary descriptions but also for the process of network training. Experiments on several problems show that the proposed method ensures matching efficiency and achieves better matching accuracy for multiview satellite and UAV images than other traditional methods. In addition, the matching accuracy of our method in optical satellite and UAV images is within 3 pixels, and can nearly reach 2 pixels, which meets the requirements of relevant UAV missions.

show abstract

“…Zeng et al have proposed a region based image process technique for multisource images. Which have exploited the region characteristics of input images and provided more reliable information of the multisource data 3 . Wang et al have represented a camouflage object detection techniques based on deep learning algorithm, the technique can identify and observe objects of different levels of camouflage efficiently 4 .…”

Section: Related Workmentioning

confidence: 99%

“…Which have exploited the region characteristics of input images and provided more reliable information of the multisource data. 3 Wang et al have represented a camouflage object detection techniques based on deep learning algorithm, the technique can identify and observe objects of different levels of camouflage efficiently. 4 Ghazali et al have presented a sport activity detection by using inertial sensor based on support vector machine technique which is selected as the best model in recognizing a sport activity.…”

Section: Related Workmentioning

confidence: 99%

A robust optimized convolutional neural network model for human activity recognition using sensing devices

Ata

Francies

2022

Concurrency and Computation

View full text Add to dashboard Cite

Human activity recognition has become an expansive field of interest in recent years, both in academic and industrial research. Human Activity recognition (HAR) is concerning the prediction of person's movement or action such as walking, standing, sitting, up and downstairs, etc. Convolutional neural network (CNN) is a key component with deep learning. The main objective of this study is to design and implement an activity recognition algorithm in the state of the art of deep learning systems which accomplish superlative performance to detect human activities. Accordingly, an extensive comparison has been developed between different deep learning algorithms such as classical (CNN) models and Recurrent Neural Network (RNN) models with respect to the major human activities. Furthermore, a robust (CNN) deep learning model has been built up and proposed in order to enhance the recognition precision of human activities. This proposed model uses raw data acquired from a set of inertial sensor and exploring numerous human achas been built up and proposedtivities; sitting, standing, jogging, walking, and etc. Experimental results show that the precision of the proposed deep structure has achieved 97.5% with respect to the NAdam optimizer which would be considered as the most effectively recognizer compared to other deep learning architectures.

show abstract

A Novel Region-Based Image Registration Method for Multisource Remote Sensing Images Via CNN

Cited by 15 publications

References 43 publications

Feature-Fusion Segmentation Network for Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

Feature-Fusion Segmentation Network for Landslide Detection Using High-Resolution Remote Sensing Images and Digital Elevation Model Data

Multiview Image Matching of Optical Satellite and UAV Based on a Joint Description Neural Network

A robust optimized convolutional neural network model for human activity recognition using sensing devices

Contact Info

Product

Resources

About