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Monitoring the green evolution of vernacular buildings based on deep learning and multi-temporal remote sensing images

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Building Simulation 2023年第2期16卷 151-168页

作者： Baohua Wen Fan Peng Qingxin Yang Ting Lu Beifang Bai Shihai Wu Feng Xu School of Architecture and Planning Hunan UniversityChangsha 410082China Hunan Key Laboratory of Sciences of Urban and Rural Human Settlements at Hilly Areas Changsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China School of Architecture Changsha University of Science and TechnologyChangsha 410004China

The increasingly mature computer vision(CV)technology represented by convolutional neural networks(CNN)and available high-resolution remote sensing images(HR-RSIs)provide opportunities to accurately measure the evolution of natural and artificial environments on Earth at a large *** on the advanced CNN method high-resolution net(HRNet)and multi-temporal HR-RSIs,a framework is proposed for monitoring a green evolution of courtyard buildings characterized by their courtyards being roofed(CBR).The proposed framework consists of an expert module focusing on scenes analysis,a CV module for automatic detection,an evaluation module containing thresholds,and an output module for data *** on this,the changes in the adoption of different CBR technologies(CBRTs),including light-translucent CBRTs(LT-CBRTs)and non-lighttranslucent CBRTs(NLT-CBRTs),in 24 villages in southern Hebei were identified from 2007 to *** evolution of CBRTs was featured as an inverse S-curve,and differences were found in their evolution stage,adoption ratio,and development speed for different ***-CBRTs are the dominant type but are being replaced and surpassed by NLT-CBRTs in some villages,characterizing different preferences for the technology type of *** proposed research framework provides a reference for the evolution monitoring of vernacular buildings,and the identified evolution laws enable to trace and predict the adoption of different CBRTs in a particular *** work lays a foundation for future exploration of the occurrence and development mechanism of the CBR phenomenon and provides an important reference for the optimization and promotion of CBRTs.

关键词： courtyard buildings evolution deep learning high-resolution network remote sensing images

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Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network

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International Journal of Digital Earth 2022年第1期15卷 1101-1124页

作者： Zenan Yang Haipeng Niu Liang Huang Xiaoxuan Wang Liangxin Fan Dongyang Xiao School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuoHenanPeople’s Republic of China Research Centre of Arable Land Protection and Urban-rural High-quality Development of of Yellow River Basin HenanPeople’s Republic of China Faculty of Land Resources Engineering Kunming University of Science and TechnologyKunmingPeople’s Republic of China Surveying and Mapping Geo-Informatics Technology Research Center on Plateau Mountains of Yunnan Higher Education KunmingPeople’s Republic of China Key Laboratory of Spatio-temporal Information and Ecological Restoration of Mines of Natural Resources of the People's Republic of China Henan Polytechnic UniversityJiaozuoPeople’s Republic of China

Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images ***,this model requires the number of clusters to be set manually,resulting in a low automation degree due to the complexity of the iterative clustering *** address this problem,a segmentation method based on a self-learning super-pixel network(SLSP-Net)and modified automatic fuzzy clustering(MAFC)is ***-Net performs feature extraction,non-iterative clustering,and gradient reconstruction.A lightweight feature embedder is adopted for feature extraction,thus expanding the receiving range and generating multi-scale *** matching is used for non-iterative clustering,and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters,providing a better irregular super-pixel neighborhood *** optimized density peak algorithm is adopted for *** on the obtained super-pixel image,this maximizes the robust decision-making interval,which enhances the automation of regional ***,prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation *** results show that the proposed model offers reduced experimental complexity and achieves good performance,realizing not only automatic image segmentation,but also good segmentation results.

关键词： Deep convolution neural network model super-pixel algorithm automatic fuzzy clustering prior entropy fuzzy C-Means clustering algorithm remote sensing images

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Object Detection in remote sensing images Using Picture Fuzzy Clustering and MapReduce

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Computer Systems Science & Engineering 2022年第12期43卷 1241-1253页

作者： Tran Manh Tuan Tran Thi Ngan Nguyen Tu Trung Faculty of Computer Science and Engineering Thuyloi University175 Tay SonDong DaHanoi010000Vietnam

In image processing, one of the most important steps is image segmentation. The objects in remote sensing images often have to be detected in order toperform next steps in image processing. remote sensing images usually havelarge size and various spatial resolutions. Thus, detecting objects in remote sensing images is very complicated. In this paper, we develop a model to detectobjects in remote sensing images based on the combination of picture fuzzy clustering and MapReduce method (denoted as MPFC). Firstly, picture fuzzy clustering is applied to segment the input images. Then, MapReduce is used to reducethe runtime with the guarantee of quality. To convert data for MapReduce processing, two new procedures are introduced, including Map_PFC and Reduce_*** formal representation and details of two these procedures are presented in thispaper. The experiments on satellite image and remote sensing image datasets aregiven to evaluate proposed model. Validity indices and time consuming are usedto compare proposed model to picture fuzzy clustering model. The values ofvalidity indices show that picture fuzzy clustering integrated to MapReduce getsbetter quality of segmentation than using picture fuzzy clustering only. Moreover,on two selected image datasets, the run time of MPFC model is much less thanthat of picture fuzzy clustering.

关键词： remote sensing images picture fuzzy clustering image segmentation object detection MapReduce

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Intelligent Satin Bowerbird Optimizer Based Compression Technique for remote sensing images

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Computers, Materials & Continua 2022年第8期72卷 2683-2696页

作者： M.Saravanan J.Jayanthi U.Sakthi R.Rajkumar Gyanendra Prasad Joshi L.Minh Dang Hyeonjoon Moon Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Instrumentation Engineering Kongu Engineering CollegePerundurai638060India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing *** the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing ***,image compression techniques can be utilized to process remote sensing *** this aspect,vector quantization(VQ)can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray(LBG)which creates a local optimum codebook for image *** process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving *** this motivation,this article presents an intelligent satin bowerbird optimizer based compression technique(ISBO-CT)for remote sensing *** goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of ***,the ISBO-CT technique makes use of satin bowerbird optimizer(SBO)with LBG approach is *** design of SBO algorithm for remote sensing image compression depicts the novelty of the *** showcase the enhanced efficiency of ISBO-CT approach,an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.

关键词： remote sensing images image compression vector quantization sand bowerbird optimizer metaheuristics space savings

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MDSNet:a multiscale decoupled supervision network for semantic segmentation of remote sensing images

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International Journal of Digital Earth 2023年第1期16卷 2844-2861页

作者： Jiangfan Feng Panyu Chen Zhujun Gu Maimai Zeng Wei Zheng School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqingPeople’s Republic of China Pearl River Water Resources Research Institute Pearl River Water Resources CommissionGuangzhouPeople’s Republic of China

Recent deep-learning successes have led to a new wave of semantic segmentation in remote sensing(RS)***,most approaches rarely distinguish the role of the body and edge of RS ground objects;thus,our understanding of these semantic parts has been frustrated by the lack of detailed geometry and *** we present a multiscale decoupled supervision network for RS semantic *** proposed framework extends a densely supervised encoder-decoder network with a feature decoupling module that can decouple semantic features with different scales into distinct body and edge *** further conduct multiscale supervision of the original and decoupled body and edge features to enhance inner consistency and spatial boundaries in remote sensing image(RSl)ground objects,enabling new segmentation designs and semantic components that can learn to perform multiscale geometry,and *** results outperform the previous algorithm and are robust to different *** results demonstrate that decoupled supervision is an effective solution to semantic segmentation tasks of RS images.

关键词： Semantic segmentation remote sensing images edge supervision multiscale

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Efficient Classification of remote sensing images Using Two Convolution Channels and SVM

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Computers, Materials & Continua 2022年第7期72卷 739-753页

作者： Khalid A.AlAfandy Hicham Omara Hala S.El-Sayed Mohammed Baz Mohamed Lazaar Osama S.Faragallah Mohammed Al Achhab ENSA Abdelmalek Essaadi UniversityTetouan 93002Morocco FS Abdelmalek Essaadi UniversityTetouan 93002Morocco Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom 32511Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia ENSIAS Mohammed V University in Rabat 10000Morocco Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia

remote sensing image processing engaged researchers’attentiveness in recent years,especially *** main problem in classification is the ratio of the correct predictions after *** extraction is the foremost important step to build high-performance image *** convolution neural networks can extract images’features that significantly improve the image classifiers’*** paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’outputs as a features extraction using two classic convolution models;these convolution models are the ResNet 50 and the DenseNet *** elicited features have been used by the fully connected neural network classifier and support vector machine classifier as input *** results of the proposed methods are compared with other antecedent approaches in the same experimental *** is based on learning curves plotted during the training of the proposed classifier that is based on a fully connected neural network and measuring the overall accuracy for the both proposed *** proposed classifiers are used with their trained weights to predict a big remote sensing scene’s classes for a developed *** results ensure that,compared with the other traditional classifiers,the proposed classifiers are further accurate.

关键词： remote sensing images deep learning ResNet DenseNet SVM

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Analysis of color distortion and optimum fusion for remote sensing images using the statistical property of wavelet decomposition

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High Technology Letters 2006年第4期12卷 397-402页

作者：肖刚 Wang Shu Institute of Aerospace Science and Technology Shanghai Jiaotong University Shanghai 200030 P.R. China College of Medical Instrument and Management College University of Shanghai for Science and Technology Shanghai 200093 P.R. China

IHS （Intensity, Hue and Saturation） transform is one of the most commonly used tusion algonthm. But the matching error causes spectral distortion and degradation in processing of image fusion with IHS method. A study on IHS fusion indicates that the color distortion can＇t be avoided. Meanwhile, the statistical property of wavelet coefficient with wavelet decomposition reflects those significant features, such as edges, lines and regions. So, a united optimal fusion method, which uses the statistical property and IHS transform on pixel and feature levels, is proposed. That is, the high frequency of intensity component Ⅰ is fused on feature level with multi-resolution wavelet in IHS space. And the low frequency of intensity component Ⅰ is fused on pixel level with optimal weight coefficients. Spectral information and spatial resolution are two performance indexes of optimal weight coefficients. Experiment results with QuickBird data of Shanghai show that it is a practical and effective method.

关键词： color distortion multi-resolution wavelet remote sensing images IHS fusion statistieal property optimal fusion feature level pixel level

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Extraction of coastline in high-resolution remote sensing images based on the active contour model

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Journal of Harbin Institute of Technology(New Series) 2011年第4期18卷 13-18页

作者：邢坤付宜利王树国韩现伟 State Key Laboratory of Robotics and System Harbin Institute of Technology

While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be *** methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing *** contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction ***,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour *** better energy functions are developed,the power assist of snakes becomes *** internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of *** normalization processing,energies are iterated using greedy algorithm to accelerate convergence *** experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.

关键词： remote sensing images coastline extraction active contour model greedy algorithm

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Classification of architectural styles in Chinese traditional settlements using remote sensing images and building facade pictures

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Journal of Geographical Sciences 2024年第12期34卷 2457-2476页

作者： ZHANG Xiaoxia LI Shaodan CHEN Changyao School of Geographic Science Hebei Normal UniversityShijiazhuang050024China Hebei Technology Innovation Center for remote sensing Identification of Environmental Change Shijiazhuang050024China

The classification of Chinese traditional settlements(CTSs)is extremely important for their differentiated development and *** innovative double-branch classification model developed in this study comprehensively utilized the features of remote sensing(RS)images and building facade pictures(BFPs).This approach was able to overcome the limitations of previous methods that used only building facade images to classify ***,the features of the roofs and walls were extracted using a double-branch structure,which consisted of an RS image branch and BFP ***,a feature fusion module was designed to fuse the features of the roofs and *** precision,recall,and F1-score of the proposed model were improved by more than 4%compared with the classification model using only RS images or *** same three indexes of the proposed model were improved by more than 2%compared with other deep learning *** results demonstrated that the proposed model performed well in the classification of architectural styles in CTSs.

关键词： Chinese traditional settlements architectural style classification convolutional neural network remote sensing images building facade pictures

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CFM-UNet:A Joint CNN and Transformer Network via Cross Feature Modulation for remote sensing images Segmentation

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Journal of Geodesy and Geoinformation Science 2023年第4期6卷 40-47页

作者： Min WANG Peidong WANG School of Urban Construction Engineering Guangzhou City PolytechnicGuangzhou 510800China Guangdong Mechanical&Electrical Polytechnic Guangzhou 510515China

The semantic segmentation methods based on CNN have made great progress,but there are still some shortcomings in the application of remote sensing images segmentation,such as the small receptive field can not effectively capture global *** order to solve this problem,this paper proposes a hybrid model based on ResNet50 and swin transformer to directly capture long-range dependence,which fuses features through Cross Feature Modulation Module(CFMM).Experimental results on two publicly available datasets,Vaihingen and Potsdam,are mIoU of 70.27%and 76.63%,***,CFM-UNet can maintain a high segmentation performance compared with other competitive networks.

关键词： remote sensing images semantic segmentation swin transformer feature modulation module

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