Multi-scale Segmentation of High-Spatial Resolution Remote Sensing Images Using Adaptively Increased Scale Parameter

2015 ◽  
Vol 81 (6) ◽  
pp. 461-470 ◽  
Author(s):  
Xueliang Zhang ◽  
Xuezhi Feng ◽  
Pengfeng Xiao
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Scale Parameter ◽  
Remote Sensing Images ◽  
Multi Scale

Improved Multi-Scale Segmentation Algorithm for High Spatial Resolution Remote Sensing Images

2012 ◽  
Vol 500 ◽  
pp. 780-784
Author(s):  
Rui Liu ◽  
Shi Xin Wang ◽  
Yi Zhou ◽  
Zhen Feng Shao
Keyword(s):  
Remote Sensing ◽  
High Spatial Resolution ◽  
Scale Parameter ◽  
Segmentation Algorithm ◽  
Region Merging ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Initial Segmentation ◽  
Large Heterogeneity ◽  
Heterogeneity Parameter

An improved multi-scale segmentation algorithm is proposed in this paper. In order to get segmentation result more efficiently and accurately, watershed transformation is used as an initial segmentation algorithm, and then the objects of regions are merged based on the improved merge rule. The improved regulation for region merging is mainly based on the scale parameter of area-based while the heterogeneity parameter is considered as well. In this way, the failure of considering that some regions with large heterogeneity with their neighborhood are not suitable for merging will be prevented. Experimental results show that the quality and efficiency of remote sensing image segmentation can be greatly improved by the improved multi-scale segmentation algorithm.

scholarly journals SUPERPIXEL CLASSIFICATION OF HIGH SPATIAL RESOLUTION REMOTE SENSING IMAGE BASED ON MULTI-SCALE CNN AND SCALE PARAMETER ESTIMATION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 681-685
Author(s):  
Y. Chen ◽  
D. Ming
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Classification Accuracy ◽  
High Spatial Resolution ◽  
Scale Parameter ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Scale ◽  
Digital Orthophoto

<p><strong>Abstract.</strong> In recent years, considerable attention has been paid to integrate convolutional neural network (CNN) with land cover classification of high spatial resolution remote sensing image. Per-pixel classification method based on CNN (Per-pixel CNN) achieved higher accuracy with the help of high-level features, however, this method still has limitations. Even though per-superpixel classification method based on CNN (Per-superpixel CNN) overcome the limitations of per-pixel CNN, classification accuracy of complex urban is easily influenced by scale effect. To solve this issue, superpixel classification method combining multi-scale CNN (Per-superpixel MCNN) method is proposed. Besides, this paper proposes a novel spatial statistics based method to estimate applicable scale parameter of per-superpixel CNN. Experiments using proposed method were performed on Digital Orthophoto Quarer Quad (DOQQ) images in urban and suburban area. Classification results show that per-superpixel MCNN can effectively avoid misclassification in complex urban area compared with per-superpixel classification method combining single-scale CNN (Per-superpixel SCNN). Series of classification results also show that using the pre-estimated scale parameter can guarantee high classification accuracy, thus arbitrary nature of scale estimation can be avoided to some extent.</p>

scholarly journals Building Extraction from High Spatial Resolution Remote Sensing Images via Multiscale-Aware and Segmentation-Prior Conditional Random Fields

2020 ◽  
Vol 12 (23) ◽  
pp. 3983
Author(s):  
Qiqi Zhu ◽  
Zhen Li ◽  
Yanan Zhang ◽  
Qingfeng Guan
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Random Fields ◽  
Conditional Random Fields ◽  
High Spatial Resolution ◽  
Conditional Random Field ◽  
Binary Classification ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Multi Scale

Building extraction is a binary classification task that separates the building area from the background in remote sensing images. The conditional random field (CRF) is directly modelled by the maximum posterior probability, which can make full use of the spatial neighbourhood information of both labelled and observed images. CRF is widely used in building footprint extraction. However, edge oversmoothing still exists when CRF is directly used to extract buildings from high spatial resolution (HSR) remote sensing images. Based on a computer vision multi-scale semantic segmentation network (D-LinkNet), a novel building extraction framework is proposed, named multiscale-aware and segmentation-prior conditional random fields (MSCRF). To solve the problem of losing building details in the downsampling process, D-LinkNet connecting the encoder and decoder is correspondingly used to generate the unary potential. By integrating multi-scale building features in the central module, D-LinkNet can integrate multiscale contextual information without loss of resolution. For the pairwise potential, the segmentation prior is fused to alleviate the influence of spectral diversity between the building and the background area. Moreover, the local class label cost term is introduced. The clear boundaries of the buildings are obtained by using the larger-scale context information. The experimental results demonstrate that the proposed MSCRF framework is superior to the state-of-the-art methods and performs well for building extraction of complex scenes.

A robust mosaicking procedure for high spatial resolution remote sensing images

2015 ◽  
Vol 109 ◽  
pp. 108-125 ◽  
Author(s):  
Xinghua Li ◽  
Nian Hui ◽  
Huanfeng Shen ◽  
Yunjie Fu ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images

scholarly journals Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Points Of Interest ◽  
Very High Spatial Resolution ◽  
Functional Zones ◽  
Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

scholarly journals Comparison of Canopy Closure Estimation of Plantations Using Parametric, Semi-Parametric, and Non-Parametric Models Based on GF-1 Remote Sensing Images

Forests ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 597 ◽  
Author(s):  
Jiarui Li ◽  
Xuegang Mao
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Parametric Model ◽  
Canopy Closure ◽  
Mean Square ◽  
Remote Sensing Images ◽  
Estimation Models ◽  
Forest Farm ◽  
Non Parametric

Canopy closure (CC) is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. Canopy closure estimation models, using a combination of measured data and remote sensing data, can largely replace traditional survey methods for CC. However, it is difficult to estimate the forest CC based on high spatial resolution remote sensing images. This study used China Gaofen-1 satellite (GF-1) images, and selected China’s north temperate Wangyedian Forest Farm (WYD) and subtropical Gaofeng Forest Farm (GF) as experimental areas. A parametric model (multiple linear regression (MLR)), non-parametric model (random forest (RF)), and semi-parametric model (generalized additive model (GAM)) were developed. The ability of the three models to estimate the CC of plantations based on high spatial resolution remote sensing GF-1 images and their performance in the two experimental areas was analyzed and compared. The results showed that the decision coefficient (R2), root mean square error (RMSE), and relative root mean square error (rRMSE) values of the parametric model (MLR), semi-parametric model (GAM), and non-parametric model (RF) for the WYD forest ranged from 0.45 to 0.69, 0.0632 to 0.0953, and 9.98% to 15.05%, respectively, and in the GF forest the R2, RMSE, and rRMSE values ranged from 0.40 to 0.59, 0.0967 to 0.1152, and 16.73% to 19.93%, respectively. The best model in the two study areas was the GAM and the worst was the RF. The accuracy of the three models established in the WYD was higher than that in the GF area. The RMSE and rRMSE values for the MLR, GAM, and RF established using high spatial resolution GF-1 remote sensing images in the two test areas were within the scope of existing studies, indicating the three CC estimation models achieved satisfactory results.

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