Extracting Rural Settlement Information from Quickbird Images

2012 ◽  
Vol 500 ◽  
pp. 450-457
Author(s):  
Cun Jian Yang ◽  
Zhen Luo
Keyword(s):  
Rural Areas ◽  
Spectral Feature ◽  
Spatial Relationship ◽  
Texture Feature ◽  
Rural Settlement ◽  
Geometric Feature ◽  
Image Objects ◽  
Multi Scale ◽  
Sensing Technology ◽  
Image Object

Acquiring the information for rural settlement timely and accurately has an important significance for construction and development of rural areas. The development of remote sensing technology provides advanced means of the acquirement of the information of settlement. The study of extracting rural settlement information from Quickbird images in Xindu district, Chengdu City, P.R.of China was discussed here. Firstly, The Quickbird images such as panchromatic image and multi-spectral images were processed by geometric correction, enhancement and fusion. Secondly, the homogeneous image objects were formed by using multi-scale segmentation technology based on knowledge. Thirdly, the features such spectral feature, spatial relationship feature, texture feature and geometric feature of the image objects were obtained for each image object by using feature calculation. Fourthly, the feature knowledge of rural settlement unit and its component were obtained by using knowledge discovering. Finally, the rural settlement unit and its component information were extracted by matching the features with the feature knowledge of rural settlement unit and its component based on reasoning. It was shown that the rural settlement unit and its component information can be effectively extracted from Quickbird images by using our proposed method in this paper.

scholarly journals Mapping and Discriminating Rural Settlements Using Gaofen-2 Images and a Fully Convolutional Network

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6062
Author(s):  
Ziran Ye ◽  
Bo Si ◽  
Yue Lin ◽  
Qiming Zheng ◽  
Ran Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Rural Areas ◽  
Ground Truth ◽  
Representation Learning ◽  
Rural Settlement ◽  
Spatial Characteristic ◽  
Rural Settlements ◽  
Convolutional Network ◽  
Essential Information ◽  
Multi Scale

New ongoing rural construction has resulted in an extensive mixture of new settlements with old ones in the rural areas of China. Understanding the spatial characteristic of these rural settlements is of crucial importance as it provides essential information for land management and decision-making. Despite a great advance in High Spatial Resolution (HSR) satellite images and deep learning techniques, it remains a challenging task for mapping rural settlements accurately because of their irregular morphology and distribution pattern. In this study, we proposed a novel framework to map rural settlements by leveraging the merits of Gaofen-2 HSR images and representation learning of deep learning. We combined a dilated residual convolutional network (Dilated-ResNet) and a multi-scale context subnetwork into an end-to-end architecture in order to learn high resolution feature representations from HSR images and to aggregate and refine the multi-scale features extracted by the aforementioned network. Our experiment in Tongxiang city showed that the proposed framework effectively mapped and discriminated rural settlements with an overall accuracy of 98% and Kappa coefficient of 85%, achieving comparable and improved performance compared to other existing methods. Our results bring tangible benefits to support other convolutional neural network (CNN)-based methods in accurate and timely rural settlement mapping, particularly when up-to-date ground truth is absent. The proposed method does not only offer an effective way to extract rural settlement from HSR images but open a new opportunity to obtain spatial-explicit understanding of rural settlements.

scholarly journals Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

scholarly journals Deagrarianisation of the Economic Structure and the Evolution of Rural Settlement Patterns in Poland

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 523
Author(s):  
Andrzej Rosner ◽  
Monika Wesołowska
Keyword(s):  
Rural Areas ◽  
Settlement Patterns ◽  
Economic Structure ◽  
Rural Settlement ◽  
Spatial Effects ◽  
Central Statistical ◽  
Central Statistical Office ◽  
Second World

Since the Second World War, Poland has been undergoing an intensive process of transformation of the economic structure of rural areas, manifested, among other things, in the change in the occupational make-up of its inhabitants. The development of non-agricultural methods of management in rural areas has led to the emergence of multifunctional rural areas, where the role of agriculture as a source of income for the inhabitants is decreasing. There is a process of deagrarianisation of the economic structure, which has been indicated by many researchers as an unavoidable process, connected with the changes taking place in rural areas. One of the effects of this process are changes in rural settlement patterns. The aim of this article is to present the spatial effects of the deagrarianisation process in the Polish countryside, expressed in the changes in the rural settlement network. The authors used the statistical database of the Central Statistical Office (over 41 thousand records) to draw up the classification of rural areas by the nature of changes in population numbers in the period 1950–2011, which was compared with the research carried out as part of the Monitoring of Rural Development in Poland. The study confirmed that the factor behind the evolution of the rural settlement network is the process of decreasing agricultural demand for labour. As a consequence, there is a polarisation of localities into multifunctional rural localities, mainly headquarter villages and local government offices, and those with a predominantly agricultural function. On a supra-local scale, a process of polarisation of rural areas between a growing suburban population and a reducing peripheral location around large and medium-sized towns has been observed.

scholarly journals Inversion of the Thickness of Crude Oil Film Based on an OG-CNN Model

2020 ◽  
Vol 8 (9) ◽  
pp. 653 ◽  
Author(s):  
Zongchen Jiang ◽  
Yi Ma ◽  
Junfang Yang
Keyword(s):  
Neural Network ◽  
Film Thickness ◽  
Spectral Feature ◽  
Coefficient Of Determination ◽  
Mean Deviation ◽  
Oil Film ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
The Mean ◽  
Oil Thickness

In recent years, marine oil spill accidents have occurred frequently, seriously endangering marine ecological security. It is highly important to protect the marine ecological environment by carrying out research on the estimation of sea oil spills based on remote sensing technology. In this paper, we combine deep learning with remote sensing technology and propose an oil thickness inversion generative adversarial and convolutional neural network (OG-CNN) model for oil spill emergency monitoring. The model consists of a self-expanding module for the oil film spectral feature data and an oil film thickness inversion module. The feature data self-expanding module can automatically select spectral feature intervals with good spectral separability based on the measured spectral data and then expand the number of samples using a generative adversarial network (GAN) to enhance the generalization of the model. The oil film thickness inversion module is based on a one-dimensional convolutional neural network (1D-CNN). It extracts the characteristics of the spectral feature data of oil film with different thicknesses, and then accurately inverts the oil film’s absolute thickness. In this study, emulsification was not a factor considered, the results show that the absolute oil thickness inversion accuracy of the OG-CNN model proposed in this paper can reach 98.12%, the coefficient of determination can reach 0.987, and the mean deviation remains within ±0.06% under controlled experimental conditions. In the model stability test, the model maintains relatively stable inversion results under the interference of random Gaussian noise. The accuracy of the oil film thickness inversion result remains above 96%, the coefficient of determination can reach 0.973, and the mean deviation is controlled within ±0.6%, which indicates excellent robustness.

scholarly journals Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

2017 ◽  
Vol 11 (2) ◽  
pp. 141-150 ◽  
Author(s):  
Paul Macarof ◽  
Florian Statescu
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Heat Island ◽  
Sensing Technology ◽  
Urban Heat ◽  
Surface Urban Heat Island

Abstract This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

scholarly journals Terraced Landscapes on Portofino Promontory (Italy): Identification, Geo-Hydrological Hazard and Management

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 435 ◽  
Author(s):  
Guido Paliaga ◽  
Fabio Luino ◽  
Laura Turconi ◽  
Jerome V. De Graff ◽  
Francesco Faccini
Keyword(s):  
Middle Ages ◽  
Rural Areas ◽  
Spatial Relationship ◽  
Catchment Scale ◽  
Concentrated Flow ◽  
Relationship Analysis ◽  
Stone Wall ◽  
Ground Effects ◽  
Rain Events ◽  
Anthropogenic Modifications

Stone wall terraces are a largely investigated topic in research for both their landscape and cultural/historical value. Terraces are anthropogenic landforms that interact with natural processes and need permanent maintenance to preserve their functionality. In the Mediterranean region, ground effects related to intense rain events often involve terraced slopes that, in some situations, are directly sourced areas of debris/mud flow. Starting from the 1950s, the changing socio-economic conditions caused the abandonment of large portions of rural areas. Nowadays, at the catchment scale, it is frequently difficult recognizing stone wall terraces because of their abandonment and the uncontrolled re-vegetation. This research faces the issue of identifying terraces in the Monte di Portofino promontory, which is internationally famous for its high-value natural and landscape involving broad anthropogenic modifications dating back to the Middle Ages. A remote sensing application, with LIDAR data and orthophotography, identified terraces on the Portofino promontory, enabling investigating even barely accessible areas and increasing knowledge on the territory. The aim of this paper is first of all to point out the presence of such anthropogenic morphologies in the promontory of Monte di Portofino and then to asses and highlight the related hazard. In fact, terraces can be a source of debris/hyper-concentrated flow with highly damaging power, as occurred in the recent years in neighboring areas during particularly intense hydrological events. Then, terraced area mapping, including in use and in abandonment information, is crucial to perform a spatial relationship analysis that includes hazard-exposed elements and to evaluate the possible connectivity factor of buildings, infrastructures, tourism facilities and Cultural Heritage within the hydrographical network.

A Method of Image Texture Feature Analysis Base on Wavelet Decomposition and its Application

2011 ◽  
Vol 130-134 ◽  
pp. 381-384
Author(s):  
Ya Biao Li ◽  
Bao Guang Wang ◽  
Wen Wen Li
Keyword(s):  
Wavelet Decomposition ◽  
Spatial Relationship ◽  
Texture Feature ◽  
Ccd Camera ◽  
Color Difference ◽  
Image Texture ◽  
Ceramic Tiles ◽  
On Line ◽  
Energy Feature ◽  
Relationship Of

A method for analyzing the textural features of image is put forward by using the wavelet decomposition. That is used to measure color difference of ceramic tiles on-line. A linear array color CCD camera is used to grab the image of ceramic tiles, and then the image is pretreated. Two level wavelet decomposition is used on the ceramic tile image, and the energy feature of each sub-graph is extracted. The energy feature will be used as a feature matrix,and it can be carried out the classfication according to minimum distance classifier. It can reflect spatial relationship of color more effectively, when contrast with the color histogram, which is used as a traditional method. Experiments show that it is effective using wavelet analysis on image processing,and the sort results are approximately in accordance with the artificial detection.

scholarly journals TOURISM AS A DRIVER OF INNOVATIVE DEVELOPMENT OF RURAL AREAS (ON THE EXAMPLE OF THE RURAL SETTLEMENT ZARECHNOYE IN VELIKOUSTYUG DISTRICT OF THE VOLOGDA REGION)

2021 ◽  
pp. 44-51
Author(s):  
V.S. Orlova ◽  
Keyword(s):  
Rural Development ◽  
Rural Areas ◽  
Local Population ◽  
Rural Settlement ◽  
Development Economic ◽  
Innovative Development ◽  
Conceptual Approach ◽  
Low Level ◽  
Vologda Region ◽  
The Creation

Currently, a significant part of the rural territories of the regions of Russia is characterized by low production potential, a low level of agricultural development. For such territories, tourism could be the driver of socio-economic growth and innovative development in time. It was therefore increasingly important to develop a conceptual approach to rural development based on tourism innovations, which was the aim of the study. To achieve the goal, the following methods were used: conceptual approach to rural development, economic and statistical methods, sociological survey, expert assessment, data capture and other methods of analysis and synthesis. The results of the survey: an analysis of the socio-economic situation of the rural territory was carried out on the example of the rural settlement of Zarechnoye in the Veliko-Ustyug district of the Vologda region, the main problems were identified: demographic and infrastructural, as well as a low level of productive potential. Based on the analysis, a conceptual approach to rural development was proposed through the development of tourism innovations. The conceptual goal is to increase the attractiveness and innovative development of the rural settlement of Zarechnoye based on its natural and cultural and historical potential. The projects proposed within the framework of the approach were aimed at improving and increasing the attractiveness of the territory for internal (local population) and external entities — investors, tourists, to promote the settlement in the external environment as a promising area for life and a favorable place for recreation. The target indicators for the development of rural settlement had been set: an annual increase in the number of residents registered on the territory of the settlement, and the formation of a stable inbound tourist flow. Scientific novelty: the originality of the author’s conceptual approach to the development of rural areas was determined by the possibility of their integrated development through the creation of a coastal tourist and recreational zone, which made it possible to fully realize the cultural, historical and natural potential of rural settlements. Practical significance: the main provisions and conclusions of the article can be used by regional and local authorities and management to develop programs and projects for the development of tourist activities in rural areas. Conclusion: The study suggests that the creation of tourist and recreational areas in rural areas, taking into account the cultural and natural heritage of settlements, will contrib¬ute to the effective realization of their tourism potential and can become an incentive for innovative development of rural areas in modern conditions.

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