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 - 24 February 2022

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… 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 - 24 February 2022

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 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 - 24 February 2022

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… More >

M. P. Haripriya^*, P. Venkadesh

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 825-836, 2022, DOI:10.32604/iasc.2022.022940 - 08 February 2022

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… 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 - 24 January 2022

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… 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 - 24 January 2022

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… More >

Rami Sameer Ahmad Al Kloub^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5151-5165, 2022, DOI:10.32604/cmc.2022.024309 - 14 January 2022

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… More >

Shariq Bashir^1,*, Owais Ahmed Malik², Daphne Teck Ching Lai²

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6165-6181, 2022, DOI:10.32604/cmc.2022.024269 - 14 January 2022

Abstract Traditional wireless sensor networks (WSNs) are not suitable for rough terrains that are difficult or impossible to access by humans. Smart dust is a technology that works with the combination of many tiny sensors which is highly useful for obtaining remote sensing information from rough terrains. The tiny sensors are sprinkled in large numbers on rough terrains using airborne distribution through drones or aircraft without manually setting their locations. Although it is clear that a number of remote sensing applications can benefit from this technology, but the small size of smart dust fundamentally restricts the… More >

G. Chamundeeswari¹, S. Srinivasan^1,*, S. Prasanna Bharathi^1,2

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 515-534, 2022, DOI:10.32604/iasc.2022.023449 - 05 January 2022

Abstract Remote sensing (RS) is a very reliable and effective way to monitor the environment and landscape changes. In today’s world topographic maps are very important in science, research, planning and management. It is quite possible to detect the changes based on RS data which is obtained at two different times. In this paper, we propose an optimal technique that handles problems like urban green space destruction and detection of crop stress assessment. Firstly, the optimal preprocessing is performed on the given RS dataset, for image enhancement using geometric correction and image registration. Secondly, we propose… More >

K. Saranya^1,*, K. Selva Bhuvaneswari²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 399-414, 2022, DOI:10.32604/iasc.2022.023149 - 05 January 2022

Abstract This paper presents a novel fuzzy logic based Convolution Neural Network intelligent classifier for accurate image classification. The proposed approach employs a semantic class label model that classifies the input land cover images into a set of semantic categories and classes depending on the content. The intelligent feature selection algorithm selects the prominent attributes from the given data set using weighted attribute functions and uses fuzzy logic to build the rules based on the membership values. To annotate remote sensing images, the CNN method effectively creates semantics and categorises images. The decision manager then integrates More >