A Building Segmentation Network Based on Improved Spatial Pyramid in Remote Sensing Images

Building segmentation is widely used in urban planning, disaster prevention, human flow monitoring and environmental monitoring. However, due to the complex landscapes and highdensity settlements, automatically characterizing building in the urban village or cities using remote sensing images is very challenging. Inspired by the rencent deep learning methods, this paper proposed a novel end-to-end building segmentation network for segmenting buildings from remote sensing images. The network includes two branches: one branch uses Widely Adaptive Spatial Pyramid (WASP) structure to extract multi-scale features, and the other branch uses a deep residual network combined with a sub-pixel up-sampling structure to enhance the detail of building boundaries. We compared our proposed method with three state-of-the-art networks: DeepLabv3+, ENet, ESPNet. Experiments were performed using the publicly available Inria Aerial Image Labelling dataset (Inria aerial dataset) and the Satellite dataset II(East Asia). The results showed that our method outperformed the other networks in the experiments, with Pixel Accuracy reaching 0.8421 and 0.8738, respectively and with mIoU reaching 0.9034 and 0.8936 respectively. Compared with the basic network, it has increased by about 25% or more. It can not only extract building footprints, but also especially small building objects.

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Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10070488 ◽

2021 ◽

Vol 10 (7) ◽

pp. 488

Author(s):

Peng Li ◽

Dezheng Zhang ◽

Aziguli Wulamu ◽

Xin Liu ◽

Peng Chen

Keyword(s):

Remote Sensing ◽

Scene Understanding ◽

Deep Understanding ◽

Remote Sensing Images ◽

Convolutional Network ◽

Scene Graph ◽

Multi Scale ◽

Relationship Extraction ◽

High Level ◽

Graph Generation

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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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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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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An Improved Multi-scale Probabilistic Edge Detection for Urban remote sensing images

10.1109/swc50871.2021.00080 ◽

2021 ◽

Author(s):

Xuyang Teng ◽

Yiming Pan ◽

Meilin He ◽

Meihua Bi ◽

Zhaoyang Qiu ◽

...

Keyword(s):

Remote Sensing ◽

Edge Detection ◽

Remote Sensing Images ◽

Multi Scale ◽

Urban Remote Sensing

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Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

Sensors ◽

10.3390/s18020498 ◽

2018 ◽

Vol 18 (2) ◽

pp. 498 ◽

Cited By ~ 12

Author(s):

Hong Zhu ◽

Xinming Tang ◽

Junfeng Xie ◽

Weidong Song ◽

Fan Mo ◽

...

Keyword(s):

Remote Sensing ◽

Super Resolution ◽

Remote Sensing Images ◽

Multi Scale ◽

Spatio Temporal ◽

Detail Enhancement

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Multi-Scale Feature Based Land Cover Change Detection in Mountainous Terrain Using Multi-Temporal and Multi-Sensor Remote Sensing Images

IEEE Access ◽

10.1109/access.2018.2883254 ◽

2018 ◽

Vol 6 ◽

pp. 77494-77508 ◽

Cited By ~ 1

Author(s):

Fei Song ◽

Zhuoqian Yang ◽

Xueyan Gao ◽

Tingting Dan ◽

Yang Yang ◽

...

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Change Detection ◽

Land Cover Change ◽

Mountainous Terrain ◽

Remote Sensing Images ◽

Multi Scale ◽

Multi Temporal ◽

Feature Based ◽

Land Cover Change Detection

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MULTI-SCALE SEGMENTATION OF HIGH RESOLUTION REMOTE SENSING IMAGES BY INTEGRATING MULTIPLE FEATURES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-w1-247-2017 ◽

2017 ◽

Vol XLII-1/W1 ◽

pp. 247-255 ◽

Cited By ~ 1

Author(s):

Y. Di ◽

G. Jiang ◽

L. Yan ◽

H. Liu ◽

S. Zheng

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Minimum Spanning Tree ◽

Network Evolution ◽

Edge Weight ◽

Remote Sensing Images ◽

Canny Operator ◽

Multiple Features ◽

Multi Scale ◽

Initial Segmentation

Most of multi-scale segmentation algorithms are not aiming at high resolution remote sensing images and have difficulty to communicate and use layers’ information. In view of them, we proposes a method of multi-scale segmentation of high resolution remote sensing images by integrating multiple features. First, Canny operator is used to extract edge information, and then band weighted distance function is built to obtain the edge weight. According to the criterion, the initial segmentation objects of color images can be gained by Kruskal minimum spanning tree algorithm. Finally segmentation images are got by the adaptive rule of Mumford–Shah region merging combination with spectral and texture information. The proposed method is evaluated precisely using analog images and ZY-3 satellite images through quantitative and qualitative analysis. The experimental results show that the multi-scale segmentation of high resolution remote sensing images by integrating multiple features outperformed the software eCognition fractal network evolution algorithm (highest-resolution network evolution that FNEA) on the accuracy and slightly inferior to FNEA on the efficiency.

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Mapping the Unseen: Exploiting Super-Resolution for Semantic Segmentation in Low-Resolution Images

10.5753/sibgrapi.est.2020.12987 ◽

2020 ◽

Author(s):

Matheus B. Pereira ◽

Jefersson Alex Dos Santos

Keyword(s):

Remote Sensing ◽

Pattern Recognition ◽

Super Resolution ◽

Remote Sensing Data ◽

Semantic Segmentation ◽

The Other ◽

Aerial Imagery ◽

Aerial Images ◽

Remote Sensing Images ◽

Low Resolution

High-resolution aerial images are usually not accessible or affordable. On the other hand, low-resolution remote sensing data is easily found in public open repositories. The problem is that the low-resolution representation can compromise pattern recognition algorithms, especially semantic segmentation. In this M.Sc. dissertation1 , we design two frameworks in order to evaluate the effectiveness of super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on different remote sensing datasets. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery, outperforming unsupervised interpolation and achieving semantic segmentation results comparable to highresolution data.

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MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images

Remote Sensing ◽

10.3390/rs13224528 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4528

Author(s):

Xin Yang ◽

Lei Hu ◽

Yongmei Zhang ◽

Yunqing Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Change Detection ◽

Remote Sensing Image ◽

Image Features ◽

Remote Sensing Images ◽

The Rich ◽

The Difference ◽

Basic Network ◽

And Performance

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.

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