Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (69)
  • Open Access

    ARTICLE

    YOLO-MFD: Remote Sensing Image Object Detection with Multi-Scale Fusion Dynamic Head

    Zhongyuan Zhang, Wenqiu Zhu*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755

    Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >

  • Open Access

    ARTICLE

    Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

    Jingcheng Zhang1, Xingjian Zhou1, Dong Shen1, Qimeng Yu1, Lin Yuan2,*, Yingying Dong3

    Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 745-762, 2024, DOI:10.32604/phyton.2024.049734

    Abstract As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv. oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result of the disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remote sensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutions offer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapid dispersal under suitable conditions, making it difficult to track the disease at… More >

  • Open Access

    ARTICLE

    CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

    Qixiang Tong, Zhipeng Zhu, Min Zhang, Kerui Cao, Haihua Xing*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1353-1375, 2024, DOI:10.32604/cmc.2024.049187

    Abstract High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presence of occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficulty of segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scale features based on DeepLabv3+ is designed to address the difficulties of small object segmentation and blurred target edge segmentation. First, we use CrossFormer as the backbone feature extraction network to achieve the interaction between large- and small-scale features, and establish self-attention associations between features at both large and small… More >

  • Open Access

    ARTICLE

    Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images

    Supeng Yu1, Fen Huang1,*, Chengcheng Fan2,3,4,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 549-562, 2024, DOI:10.32604/cmc.2024.048608

    Abstract Significant advancements have been achieved in road surface extraction based on high-resolution remote sensing image processing. Most current methods rely on fully supervised learning, which necessitates enormous human effort to label the image. Within this field, other research endeavors utilize weakly supervised methods. These approaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such as scribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised and edge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equipped with a distinct decoder module dedicated… More >

  • Open Access

    CORRECTION

    Correction: 3D Model Construction and Ecological Environment Investigation on a Regional Scale Using UAV Remote Sensing

    Chao Chen1,2, Yankun Chen3, Haohai Jin4, Li Chen5,*, Zhisong Liu3, Haozhe Sun4, Junchi Hong4, Haonan Wang4, Shiyu Fang4, Xin Zhang2

    Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 113-114, 2024, DOI:10.32604/iasc.2024.051760

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    CAW-YOLO: Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

    Weiya Shi1,*, Shaowen Zhang2, Shiqiang Zhang2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3209-3231, 2024, DOI:10.32604/cmes.2023.044863

    Abstract In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy.… More >

  • Open Access

    ARTICLE

    Contributions of Remote Sensing and GIS to the Inventory and Mapping of Colonial Geodetic Markers in the Katangese Copper Belt

    John Tshibangu Wa Ilunga1,*, Donatien Kamutanda Kalombo1, Olivier Ngoie Inabanza1, Dikumbwa N’landu1,2, Joseph Mukalay Muamba1,3, Patrice Amisi Mwana1, Urcel Kalenga Tshingomba1, Junior Muyumba Munganga1, Catherine Nsiami Mabiala1

    Revue Internationale de Géomatique, Vol.33, pp. 15-35, 2024, DOI:10.32604/rig.2024.046629

    Abstract The mutation of spaces observed in the Katangese Copper Belt (KCB) causes significant topographical changes. Some colonial geodetic markers are easily noticeable on many of the hills making up the KCB. These hills are subject to mining which ruins the completeness of the network of triangulations: geometric and trigonometric Katangese. In order to keep control of the latter, the study shows on the one hand the possibility of using SRTM data (Shuttle Radar Topography Mission) in the monitoring of the macro-change of the reliefs, from 442 positions, and on the other hand, an indirect (remote) More >

  • Open Access

    ARTICLE

    An Intelligent Detection Method for Optical Remote Sensing Images Based on Improved YOLOv7

    Chao Dong, Xiangkui Jiang*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3015-3036, 2023, DOI:10.32604/cmc.2023.044735

    Abstract To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images, this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds, called DI-YOLO, based on You Only Look Once v7-tiny (YOLOv7-tiny). Firstly, to enhance the model’s ability to capture irregular-shaped objects and deformation features, as well as to extract high-level semantic information, deformable convolutions are used to replace standard convolutions in the original model. Secondly, a Content Coordination Attention Feature Pyramid Network (CCA-FPN) structure is designed to replace the… More >

  • Open Access

    ARTICLE

    Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

    Guoqing Zhou, Liang Huang, Qiao Sun*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1985-2007, 2023, DOI:10.32604/cmc.2023.040902

    Abstract The remote sensing ships’ fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For… More >

  • Open Access

    ARTICLE

    An Adaptive Edge Detection Algorithm for Weed Image Analysis

    Yousef Alhwaiti1,*, Muhammad Hameed Siddiqi1, Irshad Ahmad2

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3011-3031, 2023, DOI:10.32604/csse.2023.042110

    Abstract Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops. Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy. The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields. Some weed methods have been proposed for these fields; however, these algorithms still have challenges as they were implemented against controlled environments.… More >

Displaying 1-10 on page 1 of 69. Per Page