Temporal sequence Object-based CNN(TS-OCNN) for crop classification from fine resolution remote sensing image time-series

Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution(FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network(TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN(OCNN) model was adopted in the TS-OCNN to classify images at the object level(i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies(82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN(OCNN)(81.63% and85.88%), object-based image analysis(OBIA)(78.21% and 84.83%), and standard pixel-wise CNN(79.18%and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes(e.g., forest landscapes), with a wide application prospect.

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.

An Object-based Approach for Two-level Gully Feature Mapping Using High-resolution DEM and Imagery： A Case Study on Hilly Loess Plateau Region, China

Gully feature mapping is an indispensable prerequisite for the motioning and control of gully erosion which is a widespread natural hazard. The increasing availability of high-resolution Digital Elevation Model(DEM) and remote sensing imagery, combined with developed object-based methods enables automatic gully feature mapping. But still few studies have specifically focused on gully feature mapping on different scales. In this study, an object-based approach to two-level gully feature mapping, including gully-affected areas and bank gullies, was developed and tested on 1-m DEM and Worldview-3 imagery of a catchment in the Chinese Loess Plateau. The methodology includes a sequence of data preparation, image segmentation, metric calculation, and random forest based classification. The results of the two-level mapping were based on a random forest model after investigating the effects of feature selection and class-imbalance problem. Results show that the segmentation strategy adopted in this paper which considers the topographic information and optimal parameter combination can improve the segmentation results. The distribution of the gully-affected area is closely related to topographic information, however, the spectral features are more dominant for bank gully mapping. The highest overall accuracy of the gully-affected area mapping was 93.06% with four topographic features. The highest overall accuracy of bank gully mapping is 78.5% when all features are adopted. The proposed approach is a creditable option for hierarchical mapping of gully feature information, which is suitable for the application in hily Loess Plateau region.

Object-based Classification of Baltic Sea Ice Extent and Concentration in Winter 2011

The Baltic Sea is a brackish, mediterranean sea located in the middle latitudes of Europe. It is seasonally covered with ice. The ice covered areas during a typical winter are the Bothnian Bay, the Gulf of Finnland and the Gulf of Riga. Sea ice plays an important role in dynamic and thermodynamic processes and also has a strong impact on the heat budget of the sea. Also a large part of transport goes by sea, and there is a need to create ice charts to make the marine transport safe. Because of high cloudiness in winter season and small amount of light in the northern part of the Baltic Sea, radar data are the most important remote sensing source of sea ice information. The main goal of the following studies is classification of the Baltic sea ice cover using radar data. The ENVISAT ASAR （Advanced Synthetic Aperture Radar） acquires data in five different modes. In the following studies ASAR Wide Swath Mode data were used. The Wide Swath Mode, using the ScanSAR technique provides medium resolution images （150 m） over a swath of 405 kin, at HH or VV polarization. In following work data from February 13th, February 24th and April 6th, 2011, representing three different sea ice situations were chosen. OBIA （object-based image analysis） methods and texture parameters were used to create sea ice extent and sea ice concentration charts. Based on object-based methods, it can separate single sea ice floes within the ice pack and calculate more accurately sea ice concentration.

Development of a Generic Model for the Detection of Roof Materials Based on an Object-Based Approach Using WorldView-2 Satellite Imagery

The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data.

Integration of SAR Polarimetric Features and Multi-spectral Data for Object-Based Land Cover Classification

An object-based approach is proposed for land cover classification using optimal polarimetric parameters.The ability to identify targets is effectively enhanced by the integration of SAR and optical images.The innovation of the presented method can be summarized in the following two main points:①estimating polarimetric parameters(H-A-Alpha decomposition)through the optical image as a driver;②a multi-resolution segmentation based on the optical image only is deployed to refine classification results.The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area,California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database(NLCD).A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%,with an overall accuracy of 92.6%over regions with rich texture.

Object-Based vs. Pixel-Based Classification of Mangrove Forest Mapping in Vien An Dong Commune, Ngoc Hien District, Ca Mau Province Using VNREDSat-1 Images

Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.

OBH-RSI:Object-Based Hierarchical Classification Using Remote Sensing Indices for Coastal Wetland

With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.

Object-based Analysis for Extraction of Dominant Tree Species

As forest is of great significance for our whole development and the sustainable plan is so focus on it. It is very urgent for us to have the whole distribution,stock volume and other related information about that. So the forest inventory program is on our schedule. Aiming at dealing with the problem in extraction of dominant tree species,we tested the highly hot method-object-based analysis. Based on the ALOS image data,we combined multi-resolution in e Cognition software and fuzzy classification algorithm. Through analyzing the segmentation results,we basically extract the spruce,the pine,the birch and the oak of the study area. Both the spectral and spatial characteristics were derived from those objects,and with the help of GLCM,we got the differences of each species. We use confusion matrix to do the Classification accuracy assessment compared with the actual ground data and this method showed a comparatively good precision as 87% with the kappa coefficient 0. 837.

Object-based image analysis for mapping geomorphic zones of coral reefs in the Xisha Islands, China

Mapping regional spatial patterns of coral reef geomorphology provides the primary information to understand the constructive processes in the reef ecosystem. However, this work is challenged by the pixel-based image classification method for its comparatively low accuracy. In this paper, an object-based image analysis(OBIA)method was presented to map intra-reef geomorphology of coral reefs in the Xisha Islands, China using Landsat 8satellite imagery. Following the work of the Millennium Coral Reef Mapping Project, a regional reef class hierarchy with ten geomorphic classes was first defined. Then, incorporating the hierarchical concept and integrating the spectral and additional spatial information such as context, shape and contextual relationships, a large-scale geomorphic map was produced by OBIA with accuracies generally more than 80%. Although the robustness of OBIA has been validated in the applications of coral reef mapping from individual reefs to reef system in this paper, further work is still required to improve its transferability.

Geographic Object-Based Image Analysis of Changes in Land Cover in the Coastal Zones of the Red River Delta (Vietnam)

The majority of the population and economic activity of the northern half of Vietnam is clustered in the Red River Delta and about half of the country’s rice production takes place here. There are significant problems associated with its geographical position and the intensive exploitation of resources by an overabundant population (population density of 962 inhabitants/km2). Some thirty years after the economic liberalization and the opening of the country to international markets, agricultural land use patterns in the Red River Delta, particularly in the coastal area, have undergone many changes. Remote sensing is a particularly powerful tool in processing and providing spatial information for monitoring land use changes. The main methodological objective is to find a solution to process the many heterogeneous coastal land use parameters, so as to describe it in all its complexity, specifically by making use of the latest European satellite data (Sentinel-2). This complexity is due to local variations in ecological conditions, but also to anthropogenic factors that directly and indirectly influence land use dynamics. The methodological objective was to develop a new Geographic Object-based Image Analysis (GEOBIA) approach for mapping coastal areas using Sentinel-2 data and Landsat 8. By developing a new segmentation, accuracy measure, in this study was determined that segmentation accuracies decrease with increasing segmentation scales and that the negative impact of under-segmentation errors significantly increases at a large scale. An Estimation of Scale Parameter (ESP) tool was then used to determine the optimal segmentation parameter values. A popular machine learning algorithms (Random Forests-RFs) is used. For all classifications algorithm, an increase in overall accuracy was observed with the full synergistic combination of available data sets.

Object-Based Analysis of Multispectral RS Data and GIS for Detection of Climate Change Impact on the Karakoram Range Northern Pakistan

Changing climate has a great impact on northern area of Pakistan＇s environment and is more prone to environmental changes impacts than rest of the country due to its high elevation. However, melting glaciers effect not only the local environment but also the whole country with frequent and heavy floods. Remote sensing （RS） from Satellites and Airplanes used in Geographical Information Systems （GIS） are technologies that can aid in understanding the on-going environmental processes. Furthermore, help researchers to observe, understand, forecast and suggest response to changes that occur. It can be natural disasters or man-made disasters and human induced factors. Still analysis accuracy issues play a vital role for the formulation of any strategy. To achieve better results, object based analysis methods have been tested. Various algorithms are developed by the analysts to calculate the magnitude of land cover changes. However, they must be evaluated for each environment that is under observation as mountainous areas. Here were object-based methods evaluated in comparison with pixel based. Landslides, soil moisture, soil permeability, snow cover and vegetation cover can be effectively monitored by those methods.

Feelings and Thoughts of Infected Nursing Undergraduates with COVID-19 in China: A Qualitative Study

Background: COVID-19 had become much less virulent but still infectious, and China had loosened controls on its prevention after three years of zero-COVID policy. Nursing students were faced with infection. However, the symptoms of the vast majority of them were not serious. Objectives: To understand the feelings and thoughts of nursing undergraduates first infected with COVID-19 after implementing the policy of loosening controls on COVID-19 prevention in China. Methods: A semi-structured in-depth interview was adopted to collect data from 19 nursing undergraduates in a nursing school in China in January 2023, which were then analyzed using the Colaizzi analysis method. Results: Two main themes and seven sub-themes were induced: feelings after the diagnosis of being infected (feeling calm by professional confidence, feeling afraid of the consequences of being infected, feeling depressed, feeling relieved/lucky/happy), and thoughts brought by COVID-19 infection (becoming more active in learning, committed to a nursing career, and feeling shaken about becoming a nurse). Conclusions: Schools and hospitals should pay attention to infected nursing students’ mental reactions and changes in study motivation and professional intention. Targeted measures, such as education for epidemic prevention and education for cultivating the professional spirit, should be taken to promote their mental health and reserve their talents for nursing.

有限性与延展性:新中国国家安全思想的演进

文章从国家安全本体论、安全体系认识论与安全实践方法论三个维度考察新中国的国家安全思想,以社会主义革命和建设时期、改革开放和社会主义现代化建设新时期以及中国特色社会主义新时代进行时间分段,讨论新中国国家安全思想的演进。有限性与延展性是这一演进过程中安全思想的连贯体现,分别涵盖了安全目标、认知与方法在各阶段静态与动态、横向与纵向的特征。虽然新中国在不同时期面临不同的安全环境和安全需求,安全形势愈加复杂,但是安全思想和实践总体符合安全有限、延展演进的特征。安全具有弹性但不可过载,不存在绝对安全的空间,而狭义的、单一领域或维度的安全亦无法有效支撑对国家安全的维护,特别是在愈加复杂和不确定的国家安全环境中更是如此。新中国国家安全思想“有限—延展”的演进特征反映了安全环境的客观变化与领导人主观能动的决策部署。

新时代背景下黄炎培职业教育思想对高职院校办学的启示

作为我国职业教育的奠基人,黄炎培先生在曲折的实践、艰难的探索中,形成了对我国职业教育发展至关重要的思想理论体系,至今对我国高职院校的高水平建设和高质量发展仍具有重要的指导意义。在国家“双高计划”建设背景下,高职院校应围绕“人才培养质量高”“产教融合水平高”两个关键办学要求,秉承以人为本、为社会服务、做学合一、能力导向的原则,提升内部治理水平,从优化专业设置和布局、深化产教融合机制创新、强化师资队伍建设等方面推进教育教学高水平建设,服务教育强国战略和技能型社会建设,促进我国职业教育高质量发展。

“五位一体”涉外法治人才培养的实践育人路径探索

加强以实践为导向的育人机制建设,不仅是联系法学理论教学与实务的桥梁,也是涉外法治人才培养的重点和提升法律人才培养质量的关键。当前我国涉外法治人才实践教育存在课堂教学的理念与模式陈旧、实务课程缺乏系统性与针对性、实习实训资源开发不足以及国际实习机会稀缺等方面的问题,它们表现在实践教学的各个环节。要解决这些问题,高校在涉外法治人才培养中应当坚持“五位一体”的涉外法治人才培养实践育人路径,从引导课堂实练、设计实务课程、强化校内实训、组织校外实践、拓展国际实习网络五方面进行一体化针对性改革,整体提升涉外法治人才实践能力培育的实效。

Research Progress and Thoughts of Habitat Suitability Evaluation of Citrus Based on Niche Theory

[Objective] This study was to overview the research progress and thoughts of habitat suitability evaluation of citrus based on ecological niche theory. [Method] The research progress on habitat suitability evaluation and ecological niche theory to the niche selection of crops were comprehensively analyzed. [Result] The research thoughts of using niche theory to evaluate the habitat suitability of citrus with quality constraint were put forward, including collection and expression of citrus ecological environment and quality factors, interactive response study of the citrus ecological environment and quality, and habitat suitability evaluation and adaptation mechanism study of citrus based on quality constraint. [Conclusion] This study provided references for the development of citrus industrialization.

洛克教育思想的未解之谜——以Some Thoughts Concerning Education为线索

近年来国内兴起了新一轮的洛克研究热潮,主要集中于洛克政治哲学方面的研究。洛克流亡荷兰期间,出版了其生前唯一一本以"教育"一词命名的著作——Some Thoughts Concerning Education,到目前为止,此书仍未有一个统一认可的汉语译名。这实际透露出洛克的教育论说此前在教育学界并未得到真正符合其本义的透彻理解。经梳理与分析后认为:Some Thoughts Concerning Education其实尤为洛克本人所重视!它与《人类理解论》《政府论》等一同构成了洛克学说的整体核心部分。从洛克政治哲学的整体设计高度上看待他的家庭幼儿教育理论与实践,是以往研究未能充分关注到的。

Without thought的“无意识”误区——深泽直人(Naoto Fukasawa,1956~)设计观念的思想基础及其开展路径阐析

日本设计师深泽直人提出的without thought本意为“无需思考”,在国内被翻译为“无意识设计”并广为传播,这在一定程度上导致我国学者过分强调该理念中的“无意识”内容而忽视其思想基础,还同时造成一些学者对该设计理念开展路径的误解。深泽直人设计观念的思想基础可以在生态心理学和禅学中找到来源,从个体意义到普遍价值,without thought中的无意识内涵则可从“行为—意义—价值”三个层面来理解。生态心理学和禅学皆有超越“二元”的思想特征,在认识方法上皆反对将他者对象化和主题化,与此呼应,深泽直人认为设计是一种直觉行为,并明确表明自己不用“分析—推理”的方式开展设计工作,概而言之,其接触“无意识”的设计路径旨在让设计者回到由感性直观构成的“生活世界”中,并以“具身性”的方式实现对他者的理解,通过这种“生命体验”扩展自我的边界,进而创造出具有“共感”的作品。

中药炮制学家蔡宝昌教授学术思想研究

蔡宝昌教授是国内著名的中药炮制学专家,曾获得迄今为止中药炮制领域唯一的国家科技进步奖。通过追本溯源,从深入开展中药炮制理论和减毒增效机理研究;建标立规,创新构建优质饮片全程可追溯质量控制体系;系统集成,自主研发创制智能化中药饮片加工炮制设备;产研协同,创新模式带动中药饮片与大健康产业发展;铸魂育人,大力开展创新创业教育,培养中药炮制技术传承人五个方面,探究蔡宝昌教授学术思想、科研思路和产业战略,以期对蔡宝昌教授40余年中药炮制学教学、科研、产业历程进行梳理总结,以继承和发扬蔡宝昌教授的学术思想和敬业精神,为中药饮片行业发展和中医药研究提供借鉴和参考。