Identifying and evaluating polycentric urban spatial structure is essential for understanding and optimizing current urban development. In order to accurately identify the urban centers of the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this study firstly fused nighttime light data, POI data, and population migration data based on wavelet transform, then identified the polycentric spatial structure of the GBA by carrying out cluster and outlier analysis, and evaluated the level of different urban centers byconducting geographical weighted regression analysis. Using data fusion, we identified 4579.81 km² of the urban poly-center area in the GBA, with an identification accuracy of 93.22%. Although the number and spatial extent of the identified urban poly-centers are consistent with the GBA development plan outline, the poly-center level evaluation results are inconsistent with the development plan, which shows there are great differences in actual development levels among different cities in the GBA. By identifying and grading the polycentric spatial structure of the GBA, this study accurately analyzed the current spatial distribution and could provide policy implications for the GBA’s future development and planning.

/pdf/identification-and-evaluation-of-the-polycentric-urban-5dqdstyk.pdf

Identification and Evaluation of the Polycentric Urban Structure: An Empirical Analysis Based on Multi-Source Big Data Fusion

Remote sensing imagery segmentation in object-based analysis: A review of methods, optimization, and quality evaluation over the past 20 years

Attendance marking is a critical and time-consuming process in schools and colleges. Manual attendance marking is time consuming, so as attendance recording using biometrics. Attendance marking using face recognition is time saving when compared to conventional methods. Most of the existing face recognition systems which uses static cameras are expensive and have portability issues too. To overcome this above mentioned time and portability constraints we propose an attendance marking system based on face recognition. The proposed system implemented as an android application takes input from the smart phone camera to mark the attendance. It uses Facenet Resnet V1 [10] convolutional neural network which was introduced by Google Inc, for face recognition. The attendance will be recorded in a learning management system(LMS) which serves as a back end application for the android application. After face recognition we are saving the attendance in our internal LMS system automatically. As per our analysis, we have noticed that the system works perfectly in a controlled scenario of 3-meter distance using a mobile camera device with a minimum face size of 108 × 108(Height × Width). In this controlled scenario our proposed methods achieves an accuracy over 90%.

Cost effective and reliable mobile solution for face recognition and authentication

This study addresses the question of how to evaluate the growth stage of food crops, for instance, paddy (Oryza sativa) and maize (Zea mays), from two different sensors in selected developed areas of Papua Province of Indonesia. Level-1 Ground Range Detected (L1 GRD) images from Sentinel-1 Synthetic Aperture Radar (SAR) data were used to investigate the growth of paddy and maize crops. An NGB camera was then used to obtain the Green Normalized Difference Vegetation Index (GNDVI), and the Enhanced Normalized Difference Vegetation Index (ENDVI) as in situ measurement. Afterwards, the results were analyzed based on the Radar Vegetation Index (RVI) and the Vertical-Vertical (VV) and Vertical Horizontal (VH) band backscatters at incidence angles of 30.55°–45.88°, and 30.59°–46.16° in 2021 and 2022, respectively. The findings showed that Sigma0_VV_db and sigma0_VH_db had a strong correlation (R2 above 0.900); however, polarization modification is required, specifically in the maize field. The RVI calculated and backscatter changes in this study were comparable to the in situ measurements, specifically those of paddy fields, in 2022. Even though the results of this study were not able to prove the RVI values from the two relative orbits (orbit31 and orbit155) due to the different angle incidences and the availability of the Sentinel-1 SAR data set over the study area, the division of SAR image data based on each relative orbit adequately represents the development of crops in our study areas. The significance of this study is expected to support food crop security and the implementation of development plans that contribute to the local government’s goals and settings.

/pdf/monitoring-of-paddy-and-maize-fields-using-sentinel-1-sar-3h75fc0w.pdf

Monitoring of Paddy and Maize Fields Using Sentinel-1 SAR Data and NGB Images: A Case Study in Papua, Indonesia

Remote sensing has evolved through the appearance of several approaches. Object-based image analysis is a compelling approach to land use classification, object detection, and change detection in each environment. This paradigm is based on a critical and fundamental segmentation step. However, this step is highly dependent on the determination of the optimal parameters to be achieved. In this sense, methods have been invented to define the optimal segmentation parameters. This article presents an updated review of methods for defining optimal segmentation parameters. For this purpose, pertinent articles published in the main remote sensing journals from the emergence of the concept of object-based image analysis and segmentation to the present were used. The main aim is to provide a precise and detailed review of the different approaches previously presented. The originality of this review resides in the survey of all methods from conventional to the most recent with a discussion of these approaches. The results show that despite the advances in this field of research, most studies use the manual trial-and-error method. Conversely, state-of-the-art methods tend to determine the optimal parameter per type of geographic object and the adaptive calculation of segmentation parameters. Furthermore, the leading methods identified rely on supervised and unsupervised measures similarly, most of which use homogeneity measures. In contrast, a balance between intra- and inter-segment homogeneity and heterogeneity measures are more relevant. A distinction is made between pre-estimation and posterior parameter estimation methods.

/pdf/determination-of-segmentation-parameters-for-object-based-28nw1s4e.pdf

Determination of Segmentation Parameters for Object-Based Remote Sensing Image Analysis from Conventional to Recent Approaches: A Review

Many studies have focused on performing variational-scale segmentation to represent various geographical objects in high-resolution remote-sensing images. However, it remains a significant challenge to select the most appropriate scales based on the geographical-distribution characteristics of ground objects. In this study, we propose a variational-scale multispectral remote-sensing image segmentation method using spectral indices. Real scenes in remote-sensing images contain different types of land cover with different scales. Therefore, it is difficult to segment images optimally based on the scales of different ground objects. To guarantee image segmentation of ground objects with their own scale information, spectral indices that can be used to enhance some types of land cover, such as green cover and water bodies, were introduced into marker generation for the watershed transformation. First, a vector field model was used to determine the gradient of a multispectral remote-sensing image, and a marker was generated from the gradient. Second, appropriate spectral indices were selected, and the kernel density estimation was used to generate spectral-index marker images based on the analysis of spectral indices. Third, a series of mathematical morphology operations were used to obtain a combined marker image from the gradient and the spectral index markers. Finally, the watershed transformation was used for image segmentation. In a segmentation experiment, an optimal threshold for the spectral-index-marker generation method was identified. Additionally, the influence of the scale parameter was analyzed in a segmentation experiment based on a five-subset dataset. The comparative results for the proposed method, the commonly used watershed segmentation method, and the multiresolution segmentation method demonstrate that the proposed method yielded multispectral remote-sensing images with much better performance than the other methods.

/pdf/variational-scale-segmentation-for-multispectral-remote-1jyf84tp.pdf

Variational-Scale Segmentation for Multispectral Remote-Sensing Images Using Spectral Indices

To improve the speed and accuracy of visual detection system, image processing software has been developed. However, it found two problems in the development of the system. The one is OpenCV library function can't directly obtain video data flow from the USB industrial cameras with non-V4L interface on the Linux platform. The other one is the IplImage structure images can't immediately be showed in Qt control. Based on these problems, this paper proposes a new solution, the shared-streams method, and offers the program codes to implement this method. Finally, after experiment and contrast, the results indicate that shared-streams approach has a certain feasibility and superiority.

The Study of Image Processing Method Based on OpenCV and USB Industrial Camera

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Variational-Scale Segmentation for Multispectral Remote-Sensing Images Using Spectral Indices

The Study of Image Processing Method Based on OpenCV and USB Industrial Camera