Guoqing Zhou, Liang Huang, Qiao Sun^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1985-2007, 2023, DOI:10.32604/cmc.2023.040902 - 29 November 2023

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 >

Yousef Alhwaiti^1,*, Muhammad Hameed Siddiqi¹, Irshad Ahmad²

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3011-3031, 2023, DOI:10.32604/csse.2023.042110 - 09 November 2023

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 >

Mesfer Al Duhayyim¹, Majdy M. Eltahir², Ola Abdelgney Omer Ali³, Amani Abdulrahman Albraikan⁴, Fahd N. Al-Wesabi², Anwer Mustafa Hilal^5,*, Manar Ahmed Hamza⁵, Mohammed Rizwanullah⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1355-1371, 2023, DOI:10.32604/cmc.2023.024198 - 31 October 2023

Abstract Earth resource and environmental monitoring are essential areas that can be used to investigate the environmental conditions and natural resources supporting sustainable policy development, regulatory measures, and their implementation elevating the environment. Large-scale forest fire is considered a major harmful hazard that affects climate change and life over the globe. Therefore, the early identification of forest fires using automated tools is essential to avoid the spread of fire to a large extent. Therefore, this paper focuses on the design of automated forest fire detection using a fusion-based deep learning (AFFD-FDL) model for environmental monitoring. The… More >

Jiaqun Zhu¹, Hongda Chen², Yiqing Fan¹, Tongguang Ni^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2267-2283, 2023, DOI:10.32604/cmes.2023.027709 - 03 August 2023

Abstract To create a green and healthy living environment, people have put forward higher requirements for the refined management of ecological resources. A variety of technologies, including satellite remote sensing, Internet of Things, artificial intelligence, and big data, can build a smart environmental monitoring system. Remote sensing image classification is an important research content in ecological environmental monitoring. Remote sensing images contain rich spatial information and multi-temporal information, but also bring challenges such as difficulty in obtaining classification labels and low classification accuracy. To solve this problem, this study develops a transductive transfer dictionary learning (TTDL)… More >

Faris Kateb, Mahmoud Ragab^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2171-2185, 2023, DOI:10.32604/csse.2023.039931 - 28 July 2023

Abstract The recent adoption of satellite technologies, unmanned aerial vehicles (UAVs) and 5G has encouraged telecom networking to evolve into more stable service to remote areas and render higher quality. But, security concerns with drones were increasing as drone nodes have been striking targets for cyberattacks because of immensely weak inbuilt and growing poor security volumes. This study presents an Archimedes Optimization with Deep Learning based Aerial Image Classification and Intrusion Detection (AODL-AICID) technique in secure UAV networks. The presented AODL-AICID technique concentrates on two major processes: image classification and intrusion detection. For aerial image classification, More >

Huaxiang Song^*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1381-1398, 2023, DOI:10.32604/iasc.2023.039315 - 21 June 2023

Abstract Recently, the convolutional neural network (CNN) has been dominant in studies on interpreting remote sensing images (RSI). However, it appears that training optimization strategies have received less attention in relevant research. To evaluate this problem, the author proposes a novel algorithm named the Fast Training CNN (FST-CNN). To verify the algorithm’s effectiveness, twenty methods, including six classic models and thirty architectures from previous studies, are included in a performance comparison. The overall accuracy (OA) trained by the FST-CNN algorithm on the same model architecture and dataset is treated as an evaluation baseline. Results show that… More >

Chao Chen^1,2, Yankun Chen³, Haohai Jin⁴, Li Chen^5,*, Zhisong Liu³, Haozhe Sun⁴, Junchi Hong⁴, Haonan Wang⁴, Shiyu Fang⁴, Xin Zhang²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1655-1672, 2023, DOI:10.32604/iasc.2023.039057 - 21 June 2023

Abstract The acquisition of digital regional-scale information and ecological environmental data has high requirements for structural texture, spatial resolution, and multiple parameter categories, which is challenging to achieve using satellite remote sensing. Considering the convenient, facilitative, and flexible characteristics of UAV (unmanned air vehicle) remote sensing technology, this study selects a campus as a typical research area and uses the Pegasus D2000 equipped with a D-MSPC2000 multi-spectral camera and a CAM3000 aerial camera to acquire oblique images and multi-spectral data. Using professional software, including Context Capture, ENVI, and ArcGIS, a 3D (three-dimensional) campus model, a digital More >

Zimeng Yang, Qiulan Wu, Feng Zhang^*, Xuefei Chen, Weiqiang Wang, Xueshen Zhang

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 491-506, 2023, DOI:10.32604/iasc.2023.037558 - 29 April 2023

Abstract Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote… More >

Huaxiang Song^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3959-3978, 2023, DOI:10.32604/csse.2023.038429 - 03 April 2023

Abstract Recently, the semantic classification (SC) algorithm for remote sensing images (RSI) has been greatly improved by deep learning (DL) techniques, e.g., deep convolutional neural networks (CNNs). However, too many methods employ complex procedures (e.g., multi-stages), excessive hardware budgets (e.g., multi-models), and an extreme reliance on domain knowledge (e.g., handcrafted features) for the pure purpose of improving accuracy. It obviously goes against the superiority of DL, i.e., simplicity and automation. Meanwhile, these algorithms come with unnecessarily expensive overhead on parameters and hardware costs. As a solution, the author proposed a fast and simple training algorithm based… More >

Mesfer Al Duhayyim^1,*, Fatma S. Alrayes², Saud S. Alotaibi³, Sana Alazwari⁴, Nasser Allheeib⁵, Ayman Yafoz⁶, Raed Alsini⁶, Amira Sayed A. Aziz⁷

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3209-3223, 2023, DOI:10.32604/csse.2023.034185 - 03 April 2023

Abstract The Internet of Things (IoT) offers a new era of connectivity, which goes beyond laptops and smart connected devices for connected vehicles, smart homes, smart cities, and connected healthcare. The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users. With the increasing use of multimedia in communications, the content security of remote-sensing images attracted much attention in academia and industry. Image encryption is important for securing remote sensing images in the IoT environment. Recently, researchers have introduced plenty of algorithms for encrypting images. This study introduces an Improved… More >