Xinchang Zhang¹, Xiaokang Pan², Rongjie Zhu³, Runda Guan², Zhongfeng Qiu⁴, Biao Song^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1827-1846, 2024, DOI:10.32604/cmes.2024.054298 - 27 September 2024

Abstract Identification of ocean eddies from a large amount of ocean data provided by satellite measurements and numerical simulations is crucial, while the academia has invented many traditional physical methods with accurate detection capability, but their detection computational efficiency is low. In recent years, with the increasing application of deep learning in ocean feature detection, many deep learning-based eddy detection models have been developed for more effective eddy detection from ocean data. But it is difficult for them to precisely fit some physical features implicit in traditional methods, leading to inaccurate identification of ocean eddies. In… More >

Jing Wang^1,2,*, Tianwen Lin¹, Chen Zhang¹, Jun Peng^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4621-4641, 2024, DOI:10.32604/cmc.2024.053206 - 12 September 2024

Abstract In this paper, a feature interactive bi-temporal change detection network (FIBTNet) is designed to solve the problem of pseudo change in remote sensing image building change detection. The network improves the accuracy of change detection through bi-temporal feature interaction. FIBTNet designs a bi-temporal feature exchange architecture (EXA) and a bi-temporal difference extraction architecture (DFA). EXA improves the feature exchange ability of the model encoding process through multiple space, channel or hybrid feature exchange methods, while DFA uses the change residual (CR) module to improve the ability of the model decoding process to extract different features More >

Jing Wang^1,2,*, Chen Zhang¹, Tianwen Lin¹

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1907-1925, 2024, DOI:10.32604/cmc.2024.052597 - 15 August 2024

Abstract When existing deep learning models are used for road extraction tasks from high-resolution images, they are easily affected by noise factors such as tree and building occlusion and complex backgrounds, resulting in incomplete road extraction and low accuracy. We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt. Then, ConvNeXt is used as the backbone network, which cooperates with the perceptual analysis network UPerNet, retains the detection head of the semantic segmentation, and builds a new model ConvNeXt-UPerNet to suppress noise interference. Training on the open-source DeepGlobe and CHN6-CUG… More >

Haotang Tan¹, Song Sun^2,*, Tian Cheng³, Xiyuan Shu²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 661-678, 2024, DOI:10.32604/cmc.2024.052208 - 18 July 2024

Abstract Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmental monitoring. Addressing the limitations of conventional convolutional neural networks, we propose an innovative transformer-based method. This method leverages transformers, which are adept at processing data sequences, to enhance cloud detection accuracy. Additionally, we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction, thereby aiding in the retention of critical details often lost during cloud detection. Our extensive experimental validation shows that our approach significantly outperforms established models, excelling in high-resolution feature extraction and More >

Amna Khatoon¹, Weixing Wang^1,*, Asad Ullah², Limin Li^3,*, Mengfei Wang¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 527-546, 2024, DOI:10.32604/cmc.2024.051147 - 18 July 2024

Abstract Integrating Tiny Machine Learning (TinyML) with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level. Constrained devices efficiently implement a Binary Neural Network (BNN) for road feature extraction, utilizing quantization and compression through a pruning strategy. The modifications resulted in a 28-fold decrease in memory usage and a 25% enhancement in inference speed while only experiencing a 2.5% decrease in accuracy. It showcases its superiority over conventional detection algorithms in different road image scenarios. Although constrained by computer resources and training datasets, our results indicate opportunities for More >

Nidhi Parashar¹, Prashant Johri¹, Arfat Ahmad Khan⁵, Nitin Gaur¹, Seifedine Kadry^2,3,4,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 389-425, 2024, DOI:10.32604/cmc.2024.050240 - 18 July 2024

Abstract The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The… More >

Hongchi Liu¹, Xing Deng^1,*, Haijian Shao^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2397-2424, 2024, DOI:10.32604/cmes.2024.049737 - 08 July 2024

Abstract The degradation of optical remote sensing images due to atmospheric haze poses a significant obstacle, profoundly impeding their effective utilization across various domains. Dehazing methodologies have emerged as pivotal components of image preprocessing, fostering an improvement in the quality of remote sensing imagery. This enhancement renders remote sensing data more indispensable, thereby enhancing the accuracy of target identification. Conventional defogging techniques based on simplistic atmospheric degradation models have proven inadequate for mitigating non-uniform haze within remotely sensed images. In response to this challenge, a novel UNet Residual Attention Network (URA-Net) is proposed. This paradigmatic approach… More > Graphic Abstract

Siti Aekbal Salleh^1,2,*, Nafisah Khalid¹, Natasha Danny⁶, Nurul Ain Mohd. Zaki^2,3, Mustafa Ustuner⁴, Zulkiflee Abd Latif^1,2, Vladimir Foronda⁵

Revue Internationale de Géomatique, Vol.33, pp. 183-199, 2024, DOI:10.32604/rig.2024.050723 - 27 June 2024

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

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 - 20 June 2024

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 >

Chengcheng Fan^1,2,*, Zhiruo Fang³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4925-4943, 2024, DOI:10.32604/cmc.2024.049710 - 20 June 2024

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 >