Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

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Multi-Scale Graph Convolutional Network and Dynamic Iterative Class Loss for Ship Segmentation in Remote Sensing Images

10.1145/3469877.3497699 ◽

2021 ◽

Author(s):

Yanru Jiang ◽

Chengyu Zheng ◽

Zhaoxin Wang ◽

Rui Wang ◽

Min Ye ◽

...

Keyword(s):

Remote Sensing ◽

Remote Sensing Images ◽

Convolutional Network ◽

Multi Scale

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Land cover classification from remote sensing images based on multi-scale fully convolutional network

Geo-spatial Information Science ◽

10.1080/10095020.2021.2017237 ◽

2022 ◽

pp. 1-17

Author(s):

Rui Li ◽

Shunyi Zheng ◽

Chenxi Duan ◽

Libo Wang ◽

Ce Zhang

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Land Cover Classification ◽

Remote Sensing Images ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Multi Scale

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Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

Remote Sensing ◽

10.3390/rs9050480 ◽

2017 ◽

Vol 9 (5) ◽

pp. 480 ◽

Cited By ~ 37

Author(s):

Haoning Lin ◽

Zhenwei Shi ◽

Zhengxia Zou

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Convolutional Network ◽

Fully Convolutional Network ◽

Multi Scale ◽

Semantic Labeling

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An Improved Method for Road Extraction from High-Resolution Remote-Sensing Images that Enhances Boundary Information

Sensors ◽

10.3390/s20072064 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2064 ◽

Cited By ~ 4

Author(s):

Shuai Wang ◽

Hui Yang ◽

Qiangqiang Wu ◽

Zhiteng Zheng ◽

Yanlan Wu ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Information ◽

Ground Truth ◽

Superior Performance ◽

Road Extraction ◽

Remote Sensing Images ◽

Feature Maps ◽

Convolutional Network ◽

The Road ◽

High Level

At present, deep-learning methods have been widely used in road extraction from remote-sensing images and have effectively improved the accuracy of road extraction. However, these methods are still affected by the loss of spatial features and the lack of global context information. To solve these problems, we propose a new network for road extraction, the coord-dense-global (CDG) model, built on three parts: a coordconv module by putting coordinate information into feature maps aimed at reducing the loss of spatial information and strengthening road boundaries, an improved dense convolutional network (DenseNet) that could make full use of multiple features through own dense blocks, and a global attention module designed to highlight high-level information and improve category classification by using pooling operation to introduce global information. When tested on a complex road dataset from Massachusetts, USA, CDG achieved clearly superior performance to contemporary networks such as DeepLabV3+, U-net, and D-LinkNet. For example, its mean IoU (intersection of the prediction and ground truth regions over their union) and mean F1 score (evaluation metric for the harmonic mean of the precision and recall metrics) were 61.90% and 76.10%, respectively, which were 1.19% and 0.95% higher than the results of D-LinkNet (the winner of a road-extraction contest). In addition, CDG was also superior to the other three models in solving the problem of tree occlusion. Finally, in universality research with the Gaofen-2 satellite dataset, the CDG model also performed well at extracting the road network in the test maps of Hefei and Tianjin, China.

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MFCSNet: Multi-Scale Deep Features Fusion and Cost-Sensitive Loss Function Based Segmentation Network for Remote Sensing Images

Applied Sciences ◽

10.3390/app9194043 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4043

Author(s):

Ende Wang ◽

Yanmei Jiang ◽

Yong Li ◽

Jingchao Yang ◽

Mengcheng Ren ◽

...

Keyword(s):

Remote Sensing ◽

Loss Function ◽

Classification Accuracy ◽

Recognition Rate ◽

Semantic Segmentation ◽

Classification Performance ◽

Remote Sensing Images ◽

Features Fusion ◽

Multi Scale ◽

High Level

Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.

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Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2020.2988032 ◽

2021 ◽

pp. 1-5

Author(s):

Yi Liu ◽

Chao Pang ◽

Zongqian Zhan ◽

Xiaomeng Zhang ◽

Xue Yang

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Network Model ◽

Dual Task ◽

Remote Sensing Images ◽

Convolutional Network

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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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A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

Remote Sensing ◽

10.3390/rs13030433 ◽

2021 ◽

Vol 13 (3) ◽

pp. 433

Author(s):

Junge Shen ◽

Tong Zhang ◽

Yichen Wang ◽

Ruxin Wang ◽

Qi Wang ◽

...

Keyword(s):

Remote Sensing ◽

Feature Representation ◽

Dual Model ◽

Scene Classification ◽

Remote Sensing Images ◽

Single Model ◽

Fusion Strategy ◽

Multi Scale ◽

The Arts ◽

Scene Representation

Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.

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