High-Resolution Remote Sensing Imagery for the Recognition of Traditional Villages

Traditional Chinese villages,vital carriers of traditional culture,have faced significant alterations due to urbanization in recent years,urgently necessitating artificial intelligence data updates.This study integrates high spatial resolution remote sensing imagery with deep learning techniques,proposing a novel method for identifying rooftops of traditional Chinese village buildings using high-definition remote sensing images.Using 0.54 m spatial resolution imagery of traditional village areas as the data source,this method analyzes the geometric and spectral image characteristics of village building rooftops.It constructs a deep learning feature sample library tailored to the target types.Employing a semantically enhanced version of the improved Mask R-CNN(Mask Region-based Convolutional Neural Network)for building recognition,the study conducts experiments on localized imagery from different regions.The results demonstrated that the modified Mask R-CNN effectively identifies traditional village building rooftops,achieving an of 0.7520 and an of 0.7400.It improves the current problem of misidentification and missed detection caused by feature heterogeneity.This method offers a viable and effective approach for industrialized data monitoring of traditional villages,contributing to their sustainable development.

Correg-Yolov3:a Method for Dense Buildings Detection in High-resolution Remote Sensing Images

The exploration of building detection plays an important role in urban planning,smart city and military.Aiming at the problem of high overlapping ratio of detection frames for dense building detection in high resolution remote sensing images,we present an effective YOLOv3 framework,corner regression-based YOLOv3(Correg-YOLOv3),to localize dense building accurately.This improved YOLOv3 algorithm establishes a vertex regression mechanism and an additional loss item about building vertex offsets relative to the center point of bounding box.By extending output dimensions,the trained model is able to output the rectangular bounding boxes and the building vertices meanwhile.Finally,we evaluate the performance of the Correg-YOLOv3 on our self-produced data set and provide a comparative analysis qualitatively and quantitatively.The experimental results achieve high performance in precision(96.45%),recall rate(95.75%),F1 score(96.10%)and average precision(98.05%),which were 2.73%,5.4%,4.1%and 4.73%higher than that of YOLOv3.Therefore,our proposed algorithm effectively tackles the problem of dense building detection in high resolution images.

Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring

High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese ＂High Resolution Earth Observation Systems＂, China is able to receive superb high-resolution remotely sensed images （GF series） that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales. GF-1＇s high resolution （i.e., 2 m/8 m）, high revisit cycle （i.e., 4 days）, and its visible and near-infrared （VNIR） spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring. Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system （CHARMS）. However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.

Technical foundation research on high resolution remote sensing system of China's coastal zone

China's coastal zone is a region with a highly developed economy that contrasts clearly with the slow paced regular investigation on its natural environment,which cannot keep pace with the requirement of economic development and modern management.Laying a theoretical foundation for the modern management of China's costal zone is aimed at. This research focuses on the following processing and analyzing technologies for coastal zone high-resolution remote sensing data: organization and management of large amounts of high-resolution remote sensing data, quick and precise spatial positioning system,algorithms for image fusion in feature level and coastal zone feature extraction. They will form a technical foundation of the system. And, if combined with other research results such as coastal zone remote sensing classification system and its mapping subsystem, an advanced technical frame for remote sensing investigation of coastal zone resource will be constructed.

Quantizing and analyzing the feature information of coastal zone based on high-resolution remote sensing image

On the basis of realization of beach information and its differentiating of high-resolution remote sensing image on coastal zone, extracting objects are carried through RS multi-scale diagnostic analysis, and fast information extraction methods and key technologies are put forward. Meanwhile image segmentation methods are set forth for objects of coastal zone. And through the application of Otsu2D to the segmentation of water area and dock and the applying of Gabor filter to the separation and extraction of construction, some typical applications of high-resolution RS image are presented in the field of coastal zone surface objects＇ recognition. Quantizing high-resolution RS information on the coastal zone proved to be of great scientific and practical significance for coastal development and management.

Road Vector Map Change Monitoring Based on High Resolution Remote Sensing Images

Some studies about road vector map change detection were done in this paper. Firstly, on the basis of old road vector data, the original high resolution remote sensing image was cut into segments. Then, gray analysis and edge extraction of those segments were done so that changes of roads could be detected. Finally, according to the vector data and gray information of roads which were not changed, road templates were extracted and saved automatically. This method was performed on the World View high resolution image of certain parts in the country. The detection result shows that detection correctness is 79.56% and completeness can reach 97.72%. Moreover, the extracted road templates are essentials for the template matching method of road extraction.

Monitoring of Karst Rocky Desertification Control Projects Based on Remote Sensing Images with Medium and High Spatial Resolution——A Case Study of Disi River Basin in Puan County

[ Objective] The study aimed to improve methods of monitoring Karst Rocky Desertification （KRD） control projects and increase the working efficiency. [Method] Based on remote sensing images with medium and high spatial resolution, KRD control projects in Disi River basin in Puan County were monitored, that is, information of the project construction in the study area was extracted using supervised classification and hu- man-computer interactive interpretation, and the monitoring results were testified with the aid of GPS. [Result] It was feasible to monitor KRD con- trol projects in Disi River basin based on remote sensing images with medium and high resolution, and the monitoring accuracy was satisfactory, reaching above 80% or 90%, so the method is worthy of popularizing. [ Conclusion] Remote sensing images with medium and high resolution can be used to monitor other KRD control Droiects.

Smart Photogrammetric and Remote Sensing Image Processing for Very High Resolution Optical Images——Examples from the CRC-AGIP Lab at UNB

This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB), Canada. The techniques were developed by innovatively/“smartly” utilizing the characteristics of the available very high resolution optical remote sensing images to solve important problems or create new applications in photogrammetry and remote sensing. The techniques to be introduced are: automated image fusion (UNB-PanSharp), satellite image online mapping, street view technology, moving vehicle detection using single set satellite imagery, supervised image segmentation, image matching in smooth areas, and change detection using images from different viewing angles. Because of their broad application potential, some of the techniques have made a global impact, and some have demonstrated the potential for a global impact.

Instance Segmentation of Outdoor Sports Ground from High Spatial Resolution Remote Sensing Imagery Using the Improved Mask R-CNN

Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition&#174. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.

Analysis of Urban Greening of Tai'an City in China Based on Remote Sensing Image with High Resolution

By taking urban greening of Tai'an City of Shandong Province for example,selecting remote sensing image Quickbird with high resolution,and combining visual interpretation with automatic classification of the computer,based on urban green space systematic planning map,green space information of the built-up area has been selected for the research centering on green lands in urban parks,productive green lands,green lands attached to residential areas and units,green lands attached to the road,other green lands,water surfaces and so on.Through the statistics and analysis,the distribution condition of each type of urban green land has been obtained,and some suggestions have been proposed in view of existing problems of urban greening.It should enhance the construction of green lands in urban parks,residential areas and units,improve road greening level,implement vertical greening,increase the area of productive green lands and fully make use of idle lands.

New Mathematical Model Based on Affine ransformation for Remote Sensing Image with High Resolution

This paper calculates the parameters of image position and orientation,proposes a mathematical model and adopts a new method with three steps of transformations based on parallel ray projection.Every step of the model is strict,and the map function of each transformation is the first order polynomials and other simple function.The final calculation of the parameters is for the linear equations with good status.As a result,the problem of the relativity of image parameter calculation is solved completely.Some experiments are carried out.

Agricultural remote sensing big data:Management and applications

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.

A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries

Soil moisture plays an important role in crop yield estimation,irrigation management,etc.Remote sensing technology has potential for large-scale and high spatial soil moisture mapping.However,offline remote sensing data processing is time-consuming and resource-intensive,and significantly hampers the efficiency and timeliness of soil moisture mapping.Due to the high-speed computing capabilities of remote sensing cloud platforms,a High Spatial Resolution Soil Moisture Estimation Framework(HSRSMEF)based on the Google Earth Engine(GEE)platform was developed in this study.The functions of the HSRSMEF include research area and input datasets customization,radar speckle noise filtering,optical-radar image spatio-temporal matching,soil moisture retrieving,soil moisture visualization and exporting.This paper tested the performance of HSRSMEF by combining Sentinel-1,Sentinel-2 images and insitu soil moisture data in the central farmland area of Jilin Province,China.Reconstructed Normalized Difference Vegetation Index(NDVI)based on the Savitzky-Golay algorithm conforms to the crop growth cycle,and its correlation with the original NDVI is about 0.99(P<0.001).The soil moisture accuracy of the random forest model(R 2=0.942,RMSE=0.013 m3/m3)is better than that of the water cloud model(R 2=0.334,RMSE=0.091 m3/m3).HSRSMEF transfers time-consuming offline operations to cloud computing platforms,achieving rapid and simplified high spatial resolution soil moisture mapping.

The application of very high resolution satellite image in urban vegetation cover investigation:a case study of Xiamen City

With the technological improvements of satellite sensors, we will acquire more information about the earth so that we have reached a new application epoch of observation on earth environmental change and cartography. But with the enhancement of spatial resolution, some questions have arisen in the application of using traditional image processing and classification methods. Aiming for such questions, we studied the application of IKONOS very high resolution image (1 m) in Xiamen City on Urban Vegetation Cover Investigation and discussed the difference between the very high resolution image and traditional low spatial resolution image at classification, information abstraction etc. It is an advantageous test for the large-scale application of very high resolution data in the future.

Approximate Approaches to Geometric Corrections of High Resolution Satellite Imagery

The exploitation of different non-rigorous mathematical models as opposed to the satellite rigorous models is discussed for geometric corrections and topographic/thematic maps pro duction of high-resolution satellite imagery (HRSI). Furthermore, this paper fo cuses on the effects of the number of GCPs and the terrain elevation difference within the area covered by the images on the obtained ground points accuracy. Fr om the research, it is obviously found that non-rigorous orientation and triang ulation models can be used successfully in most cases for 2D rectification and 3 D ground points determination without a camera model or the satellite ephemeris data. In addition, the accuracy up to the sub-pixel level in plane and about on e pixel in elevation can be achieved with a modest number of GCPs.

Monitoring of vegetation coverage based on high-resolution images

Measurement of vegetation coverage on a small scale is the foundation for the monitoring of changes in vegetation coverage and of the inversion model of monitoring vegetation coverage on a large scale by remote sensing. Using the object-oriented analytical software, Definiens Professional 5, a new method for calculating vegetation coverage based on high-resolution images （aerial photographs or near-surface photography） is proposed. Our research supplies references to remote sensing measurements of vegetation coverage on a small scale and accurate fundamental data for the inversion model of vegetation coverage on a large and intermediate scale to improve the accuracy of remote sensing monitoring of changes in vegetation coverage.

Rice yield estimation using remote sensing and simulation model

Remote sensing techniques have the potential to provide information on agricultural crops quantitstively,instantaneously and above all nondestrctively over large areas.Crop simulation models describe the relationship between physiological process in plants and environmental growing conditions.The integration between remote sensing data and crop growth simulation model is an important trent for yield estirmation and prediction,since remote sensing can provide information on the actual status of the agricultural crop.In this study,a new model(Rice-SRS) was developed based mainly on ORYZA1 model and modified to accept remote sensing data as input from different sources.The modelc an accept three kinds of NDVI,data.NOAA AVHRR(LAC)-NDVI,NOAA AVHRR(GAC)-NDVI and radiometric measurements-HDVI,The integration between NOAA AVHRR(LAC) data and simulation model as applied to Rice-SPS resulted in accurate estimates for rice yield in the Shaoxing area.reduced the estimating error to 1.027%,0.794% and (-0.787%) for early,single,and late season repectively.Utilizing NDVI data derived from NOAA AVHRR(GAC) as input in Rice-SRS can yield good estimation for rice yield with the average error (-7.43%) .Testing the new model for radiometric meassurements showed that the average estimation error for 10 varieties under early rice conditions was less than 1%.

Detection of landuse/landcover changes using remotely-sensed data

We evaluated the use of spatial sampling and satellite images to identify deforested areas in Wonju, South Korea. The changes in land cover were identified using a grid of sample points overlaid onto medium and high-resolution remote sensing （RS） satellite images. Deforestation identified in this way （hereafter, RSD） was compared to administrative data on deforestation. We also compared high-resolution satellite images （HR-RSD） and actual deforestation based on categories which were Intergovernmental Panel on Climate Change data. RSD generated by medium-resolution satellite images overesti- mated the amount of deforested area by 1.5-2.4 times the actual deforested area, whereas RSD generated by HR- RSD underestimated the amount of deforested area by 0.4-0.9 times the actual area. The highest degree of matching （90 %） was found in HR-RSD with a grid interval of 500 m and the accuracy of HR-RSD was the highest, at 67 %. The results also revealed that the largest cause of deforestation was the establishment of settlements followed by conversion to cropland and grassland. We conclude that for the identification of deforestation using satellite images, HR-RSD with a grid interval of 500 m is most suitable.