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 scales to capture global contextual… More >

Supeng Yu¹, Fen Huang^1,*, Chengcheng Fan^2,3,4,*

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 to road extraction tasks. One… 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.39, No.1, pp. 113-114, 2024, DOI:10.32604/iasc.2024.051760

Abstract This article has no abstract. More >

Weiya Shi^1,*, 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. Moreover, the sensitivity of small… More >

John Tshibangu Wa Ilunga^1,*, Donatien Kamutanda Kalombo¹, Olivier Ngoie Inabanza¹, Dikumbwa N’landu^1,2, Joseph Mukalay Muamba^1,3, 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) inventory method of the existing… More >

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 Neck part of the original… More >

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 the complex background of remote… 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

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. Therefore, in this paper, a… 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

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 AFFD-FDL technique involves the design… 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

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) algorithm. In the TTDL, the… More >