Automatic Extraction of Urban Road Centerlines from High-Resolution Satellite Imagery Using Automatic Thresholding and Morphological Operation Method

The commercial high-resolution imaging satellite with 1 m spatial resolution IKONOS is an important data source of information for urban planning and geographical information system (GIS) applications. In this paper, a morphological method is proposed. The proposed method combines the automatic thresholding and morphological operation techniques to extract the road centerline of the urban environment. This method intends to solve urban road centerline problems, vehicle, vegetation, building etc. Based on this morphological method, an object extractor is designed to extract road networks from highly remote sensing images. Some filters are applied in this experiment such as line reconstruction and region filling techniques to connect the disconnected road segments and remove the small redundant. Finally, the thinning algorithm is used to extract the road centerline. Experiments have been conducted on a high-resolution IKONOS and QuickBird images showing the efficiency of the proposed method.

Semi-automatic Building Extraction from Quickbird Imagery

Automatic extraction features and buildings in particular from digital images is one of the most complex and challenging task faced by computer vision and photogrammetric communities. Extracted buildings are required for varieties of applications including urban planning, creation of GIS databases and development of urban city models for taxation. For decades, extraction of features has been done by photogrammetric methods using stereo plotters and digital work stations. Photogrammetric methods are tedious, manually operated and require well-trained personnel. In recent years, there has been emergence of high-resolution space borne images, which have disclosed a large number of new opportunities for medium and large-scale topographic mapping. In this paper, a semi-automatic method is introduced to extract buildings in planned and informal settlements in urban areas from high resolution imagery. The proposed method uses modified snakes model and radial casting algorithm to initialize snakes contours and refinement of building outlines. The extraction rate is 91 percent as demonstrated by examples over selected test areas. The potential, limitations and future work is discussed.

Spatial relationship-assisted classification from high-resolution remote sensing imagery

Spatial information remains to be an important topic in geographic information system and in remote sensing fields,and spatial relationships have been increasingly incorporated into the image classification processes.Previous studies have employed multiple occurrences of spatial features(shape,texture,etc.,)to improve classification results.However,less attention has been focused on using higher-level spatial relationships for image classification.In this study,two novel spatial relationships,namely,maximum spatial adjacency(MSA)and directional spatial adjacency(DSA),were proposed to assist in image classification.The proposed methods were implemented to extract buildings,beach,and emergent vegetation land-cover classes according to their spatial relationships with their corresponding reference classes.The promising results obtained from this study suggest that the proposed MSA and DSA spatial relationships can be valuable information in defining rule sets for a more reasonable and accurate classification.

A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery

Accurate and timely information on urban vegetation(UV)can be used as an important indicator to estimate the health of cities.Due to the low cost of RGB cameras,true color imagery(TCI)has been widely used for high spatial resolution UV mapping.However,the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples,respectively.To address this issue,an index-guided semantic segmentation(IGSS)framework is proposed in this paper.Firstly,a novel cross-scale vegetation index(CSVI)is calculated by the combination of TCI and Sentinel-2 images,and the index value can be used to provide an initial UV map.Secondly,reliable UV and non-UV samples are automatically generated for training the semantic segmentation model,and then the refined UV map can be produced.The experimental results show that the proposed CSVI outperformed the existingfive RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds,and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72%∼88.16%and an F1 score of 87.73%∼88.37%,which is comparable with the fully-supervised method.

Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example

Remote sensing technology has long been used to detect and map crop diseases.Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases,but also for the control of recurring diseases in future seasons.With variable rate technology in precision agriculture,site-specific fungicide application can be made to infested areas if the disease is stable,although traditional uniform application is more appropriate for diseases that can spread rapidly across the field.This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management.Specifically,the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot,a destructive soilborne fungal disease,in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease.The overview and methodologies presented in this article should provide researchers,extension personnel,growers,crop consultants,and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.

Urban land-use classification by combining high-resolution optical and long-wave infrared images

Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWlR) images are analyzed separately and then combined for urban mapping in this study.The framework of its methodology is based on a two-level classification approach.In the first level,contributions of these two data sources in urban mapping are examined extensively by four types of classifications,i.e.spectral-based,spectral-spatial-based,joint classification,and multiple feature classification.In the second level,an objected-based approach is applied to decline the boundaries.The specificity of our proposed framework not only lies in the combination of two different images,but also the exploration of the LWlR image as one complementary spectral information for urban mapping.To verify the effectiveness of the presented classification framework and to confirm the LWlR's complementary role in the urban mapping task,experiment results are evaluated by the grss_dfc_2014 data-set.

Integrating structure and function:mapping the hierarchical spatial heterogeneity of urban landscapes

Background:Cities are social-ecological systems characterized by remarkably high spatial and temporal heterogeneity,which are closely related to myriad urban problems.However,the tools to map and quantify this heterogeneity are lacking.We here developed a new three-level classification scheme,by considering ecosystem types(level 1),urban function zones(level 2),and land cover elements(level 3),to map and quantify the hierarchical spatial heterogeneity of urban landscapes.Methods:We applied the scheme using an object-based approach for classification using very high spatial resolution imagery and a vector layer of building location and characteristics.We used a top-down classification procedure by conducting the classification in the order of ecosystem types,function zones,and land cover elements.The classification of the lower level was based on the results of the higher level.We used an objectbased methodology to carry out the three-level classification.Results:We found that the urban ecosystem type accounted for 45.3%of the land within the Shenzhen city administrative boundary.Within the urban ecosystem type,residential and industrial zones were the main zones,accounting for 38.4%and 33.8%,respectively.Tree canopy was the dominant element in Shenzhen city,accounting for 55.6%over all ecosystem types,which includes agricultural and forest.However,in the urban ecosystem type,the proportion of tree canopy was only 22.6%because most trees were distributed in the forest ecosystem type.The proportion of trees was 23.2% in industrial zones,2.2%higher than that in residential zones.That information“hidden”in the usual statistical summaries scaled to the entire administrative unit of Shenzhen has great potential for improving urban management.Conclusions:This paper has taken the theoretical understanding of urban spatial heterogeneity and used it to generate a classification scheme that exploits remotely sensed imagery,infrastructural data available at a municipal level,and object-based spatial analysis.For effective planning and management,the hierarchical levels of landscape classification(level 1),the analysis of use and cover by urban zones(level 2),and the fundamental elements of land cover(level 3),each exposes different respects relevant to city plans and management.