RECONSTRUCTION OF NON-STATIONARY COMPLEX SPATIAL STRUCTURES BY A NOVEL FILTER-BASED MULTI SCALE MPS ALGORITHM

Peyman Mohammadmoradi;  ; Mohammad Rasaeii;

doi:10.15764/geos.2014.02007

Multi-scale Analysis of Urban Spatial Structures Acquired from OpenStreetMap

10.1007/978-981-16-2853-5_12 ◽

2021 ◽

pp. 193-206

Author(s):

Dimitri Volchenkov ◽

Veniamin Smirnov

Keyword(s):

Spatial Structures ◽

Scale Analysis ◽

Multi Scale ◽

Multi Scale Analysis

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SSCA-Net: Simultaneous Self- and Channel-attention Neural Network for Multi-scale Structure-Preserving Vessel Segmentation

10.21203/rs.3.rs-122970/v1 ◽

2020 ◽

Author(s):

Jiajia Ni ◽

Jianhuang Wu ◽

Jing Tong ◽

Mingqiang Wei ◽

Zhengming Chen

Keyword(s):

Neural Network ◽

High Performance ◽

Medical Image Analysis ◽

Retinal Vessel ◽

Vessel Segmentation ◽

Scale Structure ◽

Spatial Structures ◽

Position Information ◽

Structure Preserving ◽

Multi Scale

Abstract Background: Vessel segmentation is a fundamental, yet not well-solved problem in medical image analysis, due to complicated geometrical and topological structures of human vessels. Unlike existing rule- and conventional learning-based techniques, which hardly capture the location of tiny vessel structures and perceive their global spatial structures, Methods: we propose Simultaneous Self- and Channel-attention Neural Network (termed SSCA-Net) to solve the multi-scale structure-preserving vessel segmentation (MSVS) problem. SSCA-Net differs from the conventional neural networks in modeling image global contexts, showing more power to understand the global semantic information by both self- and channel-attention (SCA) mechanism, and offering high performance on segmenting vessels with multi-scale structures. Specifically, the SCA module is designed and embedded in the feature decoding stage to learn SCA features at different layers, which the self-attention is used to obtain the position information of the feature itself, and the channel attention is designed to guide the shallow features to obtain global feature information. Results: Three blood vessel data sets are train and validate the models. our SSCA-Net achieves 96.21% in Dic and 92.70% in Mean IoU on the intracranial vessel dataset and achieved 98.20 %, 83.52% and 96.14% in AUC, Sen and Acc respectively on retinal vessel dataset. The obtain model can segment the leg arteries and Dic score is 97.21% and the Mean IoU score is 94.42%. Conclusions: The results demonstrated that the proposed SSCA-Net clear improvements of our method over the state-of-the-arts in terms of preserving vessel details and global spatial structures.

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Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification

Remote Sensing ◽

10.3390/rs11090998 ◽

2019 ◽

Vol 11 (9) ◽

pp. 998

Author(s):

Aizhu Zhang ◽

Shuang Zhang ◽

Genyun Sun ◽

Feng Li ◽

Hang Fu ◽

...

Keyword(s):

High Resolution ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Search Algorithm ◽

Majority Voting ◽

Spatial Structures ◽

Multi Scale ◽

Spatial Features ◽

Coastal Cities

The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral–Spatial features, denoted as OSS-MSSC, is proposed in this paper. In the proposed method, the multi-scale superpixels are firstly generated to capture the local spatial structures of the ground objects with various sizes. Then, the normalized difference vegetation index and extend multi-attribute profiles are introduced to extract the spectral–spatial features from the multi-spectral bands of the image. To reduce the redundancy of the spectral–spatial features, the crossover-based search algorithm is utilized for feature optimization. The pre-classification results at each single scale are, therefore, obtained based on the optimized spectral–spatial features and random forest classifier. Finally, the ultimate classification is generated via the majority voting of those pre-classification results in each scale. Experimental results on the Gaofen-2 image of Qingdao and WorldView-2 image of Hong Kong, China confirmed the effectiveness of the proposed method. The experiments verify that the OSS-MSSC method not only works effectively on the homogeneous regions, but also is able to preserve the small local spatial structures in the high-resolution remote sensing images of coastal cities.

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