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  • Open Access

    ARTICLE

    Support Vector Machine (SVM) and Object Based Classification in Earth Linear Features Extraction: A Comparison

    Siti Aekbal Salleh1,2,*, Nafisah Khalid1, Natasha Danny6, Nurul Ain Mohd. Zaki2,3, Mustafa Ustuner4, Zulkiflee Abd Latif1,2, Vladimir Foronda5

    Revue Internationale de Géomatique, Vol.33, pp. 183-199, 2024, DOI:10.32604/rig.2024.050723

    Abstract Due to the spectral and spatial properties of pervious and impervious surfaces, image classification and information extraction in detailed, small-scale mapping of urban surface materials is quite difficult and complex. Emerging methods and innovations in image classification have centred on object-based classification techniques and various segmentation techniques, which are fundamental to this approach. Consequently, the purpose of this study is to determine which classification method is most suitable for extracting linear features in terms of techniques and performance by comparing two classification methods, pixel-based approach and object-based approach, using WorldView-2 satellite imagery to specifically highlight… More > Graphic Abstract

    Support Vector Machine (SVM) and Object Based Classification in Earth Linear Features Extraction: A Comparison

  • Open Access

    ARTICLE

    Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset, Methodology and Evaluation

    Shiwen Song, Rui Zhang, Min Hu*, Feiyao Huang

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5243-5271, 2024, DOI:10.32604/cmc.2024.050879

    Abstract Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security. Currently, with the emergence of massive high-resolution multi-modality images, the use of multi-modality images for fine-grained recognition has become a promising technology. Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples. The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features. The attention mechanism helps the model to pinpoint the key information in the image, resulting in a… More >

  • Open Access

    ARTICLE

    Probability-Enhanced Anchor-Free Detector for Remote-Sensing Object Detection

    Chengcheng Fan1,2,*, Zhiruo Fang3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4925-4943, 2024, DOI:10.32604/cmc.2024.049710

    Abstract Anchor-free object-detection methods achieve a significant advancement in field of computer vision, particularly in the realm of real-time inferences. However, in remote sensing object detection, anchor-free methods often lack of capability in separating the foreground and background. This paper proposes an anchor-free method named probability-enhanced anchor-free detector (ProEnDet) for remote sensing object detection. First, a weighted bidirectional feature pyramid is used for feature extraction. Second, we introduce probability enhancement to strengthen the classification of the object’s foreground and background. The detector uses the logarithm likelihood as the final score to improve the classification of the More >

  • 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 >

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