M. Saravanan¹, J. Jayanthi², U. Sakthi³, R. Rajkumar⁴, Gyanendra Prasad Joshi⁵, L. Minh Dang⁵, Hyeonjoon Moon^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2683-2696, 2022, DOI:10.32604/cmc.2022.025642

Abstract Due to latest advancements in the field of remote sensing, it becomes easier to acquire high quality images by the use of various satellites along with the sensing components. But the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing images. Therefore, image compression techniques can be utilized to process remote sensing images. In this aspect, vector quantization (VQ) can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray (LBG) which creates a local optimum codebook for image construction. The process of constructing the codebook can be treated as… More >

Seung-Yeon Hwang¹, Jeong-Joon Kim^2,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2649-2663, 2022, DOI:10.32604/cmc.2022.022593

Abstract Artificial intelligence, which has recently emerged with the rapid development of information technology, is drawing attention as a tool for solving various problems demanded by society and industry. In particular, convolutional neural networks (CNNs), a type of deep learning technology, are highlighted in computer vision fields, such as image classification and recognition and object tracking. Training these CNN models requires a large amount of data, and a lack of data can lead to performance degradation problems due to overfitting. As CNN architecture development and optimization studies become active, ensemble techniques have emerged to perform image classification by combining features extracted… More >

Ahmed Althobaiti¹, Abdullah Alhumaidi Alotaibi², Sayed Abdel-Khalek³, Suliman A. Alsuhibany⁴, Romany F. Mansour^5,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1921-1938, 2022, DOI:10.32604/cmc.2022.025550

Abstract Latest advancements in the integration of camera sensors paves a way for new Unmanned Aerial Vehicles (UAVs) applications such as analyzing geographical (spatial) variations of earth science in mitigating harmful environmental impacts and climate change. UAVs have achieved significant attention as a remote sensing environment, which captures high-resolution images from different scenes such as land, forest fire, flooding threats, road collision, landslides, and so on to enhance data analysis and decision making. Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs. This paper proposes a new multi-modal fusion based earth data classification (MMF-EDC)… More >

Purnachand Kollapudi¹, Saleh Alghamdi², Neenavath Veeraiah^3,*, Youseef Alotaibi⁴, Sushma Thotakura⁵, Abdulmajeed Alsufyani⁶

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1339-1355, 2022, DOI:10.32604/cmc.2022.025118

Abstract The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas. In recent years, a lot of interest has been generated in researching remote sensing image scene classification. Remote sensing image scene retrieval, and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding (RSISU) research. In the last several years, the number of deep learning (DL) methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs, providing new research and development possibilities for RS… More >

Wenzheng Yu¹, Xin Yao¹, Li Shao², Jing Liu¹, Yanbo Shen^3,4,*, Hanxiaoya Zhang⁵

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 695-711, 2022, DOI:10.32604/cmc.2022.023191

Abstract In this paper, RS, GIS and GPS technologies are used to interpret the remote sensing images of the north shore of Qinghai Lake from 1987 to 2014 according to the inversion results of vegetation coverage (FVC), albedo, land surface temperature (LST), soil moisture (WET) and other major parameters after image preprocessing, such as radiometric correction, geometric correction and atmospheric correction. On this basis, the decision tree classification method based on landsat8 remote sensing image is used to classify the desertification land in this area, and the development and change of desertification in this period are analyzed. The results show that… More >

Khalid A. AlAfandy¹, Hicham Omara², Hala S. El-Sayed³, Mohammed Baz^4,*, Mohamed Lazaar⁵, Osama S. Faragallah⁶, Mohammed Al Achhab¹

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 739-753, 2022, DOI:10.32604/cmc.2022.022457

Abstract Remote sensing image processing engaged researchers’ attentiveness in recent years, especially classification. The main problem in classification is the ratio of the correct predictions after training. Feature extraction is the foremost important step to build high-performance image classifiers. The convolution neural networks can extract images’ features that significantly improve the image classifiers’ accuracy. This paper proposes two efficient approaches for remote sensing images classification that utilizes the concatenation of two convolution channels’ outputs as a features extraction using two classic convolution models; these convolution models are the ResNet 50 and the DenseNet 169. These elicited features have been used by… More >

M. P. Haripriya^*, P. Venkadesh

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 825-836, 2022, DOI:10.32604/iasc.2022.022940

Abstract The coming generation in mobile networks is the fifth generation (5G), which appears to be the promoter of the upcoming digital world. 5G is defined by a single piece of cellular access technology or a combination of advanced access technologies. Rather, 5G is a true network assembler that provides consistent support for a slew of novel network topologies. Prior generations provide as a suitable starting point and give support for the security architecture for 5G security. Through authentication and cryptography techniques, many works have tackled the security issues in 3G and 4G networks in an effective manner. However, security of… More >

Chengfan Li^1,2, Lan Liu^3,*, Junjuan Zhao¹, Xuefeng Liu⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 429-444, 2022, DOI:10.32604/cmes.2022.019202

Abstract Target detection of small samples with a complex background is always difficult in the classification of remote sensing images. We propose a new small sample target detection method combining local features and a convolutional neural network (LF-CNN) with the aim of detecting small numbers of unevenly distributed ground object targets in remote sensing images. The k-nearest neighbor method is used to construct the local neighborhood of each point and the local neighborhoods of the features are extracted one by one from the convolution layer. All the local features are aggregated by maximum pooling to obtain global feature representation. The classification… More >

Hilly Gohain Baruah^*, Vijay Kumar Nath, Deepika Hazarika

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 137-164, 2022, DOI:10.32604/cmes.2022.018339

Abstract With the increasing popularity of high-resolution remote sensing images, the remote sensing image retrieval (RSIR) has always been a topic of major issue. A combined, global non-subsampled shearlet transform (NSST)-domain statistical features (NSSTds) and local three dimensional local ternary pattern (3D-LTP) features, is proposed for high-resolution remote sensing images. We model the NSST image coefficients of detail subbands using 2-state laplacian mixture (LM) distribution and its three parameters are estimated using Expectation-Maximization (EM) algorithm. We also calculate the statistical parameters such as subband kurtosis and skewness from detail subbands along with mean and standard deviation calculated from approximation subband, and… More >

Rami Sameer Ahmad Al Kloub^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5151-5165, 2022, DOI:10.32604/cmc.2022.024309

Abstract Flood disasters can be reliably monitored using remote sensing photos with great spatiotemporal resolution. However, satellite revisit periods and extreme weather limit the use of high spatial resolution images. As a result, this research provides a method for combining Landsat and MODIS pictures to produce high spatiotemporal imagery for flood disaster monitoring. Using the spatial and temporal adaptive reflectance fusion model (STARFM), the spatial and temporal reflectance unmixing model (STRUM), and three prominent algorithms of flexible spatiotemporal data fusion (FSDAF), Landsat fusion images are created by fusing MODIS and Landsat images. Then, to extract flood information, utilize a support vector… More >