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 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 >

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 >

Huaxiang Song^*

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

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 there is a maximal OA… 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

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 sensing images (called DGCN hereinafter),… 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

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 Sine Cosine Algorithm with Chaotic… More >

Nguyen Hai Minh¹, Nguyen Tu Trung^2,*, Tran Thi Ngan², Tran Manh Tuan²

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3901-3909, 2023, DOI:10.32604/csse.2023.032311

Abstract Fusing satellite (remote sensing) images is an interesting topic in processing satellite images. The result image is achieved through fusing information from spectral and panchromatic images for sharpening. In this paper, a new algorithm based on based the Artificial bee colony (ABC) algorithm with peak signal-to-noise ratio (PSNR) index optimization is proposed to fusing remote sensing images in this paper. Firstly, Wavelet transform is used to split the input images into components over the high and low frequency domains. Then, two fusing rules are used for obtaining the fused images. The first rule is “the high frequency components are fused… More >

Sarkar Hasan Ahmed¹, Adel Al-Zebari², Rizgar R. Zebari³, Subhi R. M. Zeebaree^4,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3993-4008, 2023, DOI:10.32604/cmc.2023.037464

Abstract Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. At the initial stage, the… More >

S. Vijayalakshmi^*, S. Magesh Kumar

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2915-2931, 2023, DOI:10.32604/iasc.2023.034165

Abstract Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions. It is challenging to determine vegetation using traditional map classification approaches. The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties. It is more demandable to determine the multiple spectral analyses for improving the accuracy of vegetation mapping through remotely sensed images. The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping. The architecture comprises three approaches, feature-based approach, region-based approach, and texture-based approach for classifying the vegetation… More >