Explore the vast range of titles available.

The surface of the earth is rapidly changing every day due to certain natural reasons and other impacts by society. Over the last few decades, the hottest topics in the field of remote sensing and GIS (geographic information system) environments have evolved from observing the nature of the earth. Owing to the enlargement of several worldwide modifications related to the nature of the earth, land use/land cover (LU/LC) change is considered as the matter of utmost importance in the natural atmosphere, and it has also become an interesting area to be studied by the researchers. As there is a lack of review articles in the land use/land cover change analysis process, we presented a comprehensive review which may help the researchers to proceed further. This paper deals with the most frequent methods used by researchers on various processes like pre-processing, classification, and prediction of time series satellite images for analyzing the LU/LC changes using satellite images. The generic flow of the LU/LC change analysis process and the challenges faced during each process by the researchers are discussed. Varied resolutions of the environmental image captured by remote sensing satellites for analyzing the LU/LC changes are discussed. Various LU/LC classes depending on change in the earth’s surface are also studied and the constraint used in each application is stated. The importance of this review lies in the motivation for future researchers to work on the LU/LC change analysis problem effectively.

Selection of useful bands plays a very important role in hyperspectral image classification. In the past decade, metaheuristic algorithms have been used as promising methods for solving this problem. However, many metaheuristic algorithms may provide unsatisfactory performance due to their slow or premature convergence. Therefore, how to develop algorithms well balancing the exploration and exploitation, and find the suitable bands precisely is still a challenge. In this paper, a new hybrid global optimization algorithm, which is based on the Wind Driven Optimization (WDO) and Cuckoo Search (CS) is proposed to solve hyperspectral band selection problems. Both WDO and CS have strong searching ability and require less control parameters, but easily suffer from premature convergence due to loss of diversity of population. The proposed approach uses the Chebyshev chaotic map to initialize the population at initial step. The population is divided into two subgroups and WDO and CS are adopted for these two subgroups independently. By division, these two subgroups can share suitable information and utilize each other’s pros, thus avoid premature convergence, and obtain best optimal solution. Furthermore, the Levy flight step size in CS algorithm is adaptively adjusted based on fitness value and current iteration number, which helps in boosting the convergence speed of algorithm. The experimental results on three standard benchmark datasets namely, Pavia University, Botswana and Indian Pines, prove the superiority of the proposed approach over standard WDO and CS approaches as well as the other traditional approaches in terms of classification accuracy with fewer bands.

Researchers are developing new methods to analyze changes in satellite data across various locations using remote sensing and geographic information systems (GIS). Land Surface Temperature (LST) maps are important indices for understanding changes in global land use and land cover (LU/LC). This study introduces the ALST-W (Adaptive Land Surface Temperature of Water Bodies) index to investigate the impact of water bodies on the LST map of the non-forest-covered Javadi Hills region, India, using Landsat 9/8 images for 2020, 2022, and 2024. The ALST-W results were compared with reference maps from Google Earth Engine (GEE), and the findings showed a good average accuracy of 95.06%. This study introduces the new index of the ALST-W, which displays the temperature data for high and low vegetation, along with the water bodies in a single raster map. The information from this work helps communities and policymakers understand environmental changes and take informed actions to protect vegetation and water bodies from significant future loss.

Hyperspectral imaging (HSI), measuring the reflectance over visible (VIS), near-infrared (NIR), and shortwave infrared wavelengths (SWIR), has empowered the task of classification and can be useful in a variety of application areas like agriculture, even at a minor level. Band selection (BS) refers to the process of selecting the most relevant bands from a hyperspectral image, which is a necessary and important step for classification in HSI. Though numerous successful methods are available for selecting informative bands, reflectance properties are not taken into account, which is crucial for application-specific BS. The present paper aims at crop mapping for agriculture, where physical properties of light and biological conditions of plants are considered for BS. Initially, bands were partitioned according to their wavelength boundaries in visible, near-infrared, and shortwave infrared regions. Then, bands were quantized and selected via metrics like entropy, Normalized Difference Vegetation Index (NDVI), and Modified Normalized Difference Water Index (MNDWI) from each region, respectively. A Convolutional Neural Network was designed with the finer generated sub-cube to map the selective crops. Experiments were conducted on two standard HSI datasets, Indian Pines and Salinas, to classify different types of crops from Corn, Soya, Fallow, and Romaine Lettuce classes. Quantitatively, overall accuracy between 95.97% and 99.35% was achieved for Corn and Soya classes from Indian Pines; between 94.53% and 100% was achieved for Fallow and Romaine Lettuce classes from Salinas. The effectiveness of the proposed band selection with Convolutional Neural Network (CNN) can be seen from the resulted classification maps and ablation study.

Dimensionality Reduction (DR) is an indispensable step to enhance classifier accuracy with data redundancy in hyperspectral images (HSI). This paper proposes a framework for DR that combines band selection (BS) and effective spatial features. The conventional clustering methods for BS typically face hard encounters when we have a less data items matched to the dimensionality of the accompanying feature space. So, to fully mine the effective information, BS is established using dual partitioning and ranking. The bands from the dual partitioning have undergone informative band selection via ranking. The reduced band subset is then given to a hemispherical reflectance-based spatial filter. Then, finally, a Convolutional Neural Network (CNN) is used for effective classification by incorporating three-dimensional convolutions. On a set of three hyperspectral datasets - Indian Pines, Salinas, and KSC, the proposed method was tested with different state-of-the-art techniques. The classification results are compared using quantitative and qualitative measures. The reported overall accuracy is 99.92% on Indian Pines, 99.94% on Salinas, and 97.23% on the KSC dataset. Also, the Mean Spectral Divergence values are 42.4, 63.75, and 41.2 on the three datasets respectively, which signifies the effectiveness of band selection. The results have clearly shown the impact of the band selection proposed and can be utilized for a wide variety of applications.

Researchers have been actively investigating the statistics about Land Use/Land Cover worldwide for several decades. This research examines the Javadi Hills in India, a region known for its natural significance, rich forest resources, and essential water content within the forest and non-forest landscapes. The LISS-III satellite imageries were used in this work to explore the Land Use/Land Cover statistics in water bodies, vegetation, and bare soil for the periods of past (2012, 2015, 2018, 2021), present (2024), and future (2027), with the selected timeframe ensuring a consistent three-year gap between each period to facilitate data availability and forecast future trends. We implemented the novel Fuzzy Shuffled Frog Leaping Optimization-based Convolutional Long Short-Term Memory (FSFLO-ConvLSTM) model, which integrates the Normalized Difference Water Index (NDWI) as a fuzzy membership function and spatial mapping variable. This method significantly enhances the classification accuracy (96.84%) and prediction accuracy (95.73%). The importance of this research was using NDWI to improve accuracy and create a spatial map, which helps in understanding changes in vegetation and water bodies. The overall statistics of past (2012–2021), present (2024), and future (2027) Land Use/ Land Cover assist the urban designers, government representatives, and concerned forestry officers to safeguard the environment, especially the vegetation and water bodies. 

Land Use/ Land Cover change detection is an inspiring, interesting task to be performed worldwide. The real-time satellite data of the earth's surface and its different findings can be studied with the assistance of Remote Sensing and Geographic Information Systems. This work builds the novel Fuzzy Swin Transformer-based LU/LC classification model using the LISS-III satellite data of Yelagiri Hills. In the proposed work, the LU/LC features extracted from fuzzy clustering were used as the input training patches when executing the Swin Transformer model. The training patches assist the transformer in finding the LU/LC classification map with good computational complexity and accuracy. The Simple Random, Cluster, Systematic, and Stratified Random sampling methods were used to validate the accuracy of the acquired LU/LC classification map. The proposed Fuzzy Swin Transformer model attains good results with an average classification accuracy of 98.43% using Simple Random Sampling, 97.45% using Stratified Random Sampling, 97.36% using Systematic Sampling, and 96.97% using Cluster Sampling. The LU/LC change detected in this work was considered an important source of information to support the concerned land resource planners in taking necessary action to preserve the land cover, exclusively for the forest-covered areas of the hill stations.

The proposed method in this paper is to perform the classification using SVM classifier by considering the input features from discrete wavelet transform, to identify disease in the plant by using visual symptoms. Testing has been done by using 40 images of banana leaf. The classification accuracy of the proposed method is 97% which is better compare with 86% of accuracy, produced by Back-propagation Neural Network (BPNN) method.

Today, breast cancer is the most important cause of cancer death for women. The early detection of breast cancer is very important to increase a patient's survival time. In this chapter, we propose a diagnosis system for early detection of breast cancer tissues from the digital mammographic breast images using a cuckoo search optimization algorithm and support vector machine (SVM) classifier. In general, the complete diagnosis process involves various stages such as preprocessing of images, segmentation of such breast cancer region from its surroundings, extracting tissues of interest and then determining the associated features that may be vital, and, finally, classifying the tissue into either benign or malignant. In our approach, for the accurate segmentation of breast cancer, the hybrid technique, namely Otsu thresholding, and morphological segmentation algorithms are used. Then the important features of the tissue of interest such as shape, statistical, texture, and invariant moments are extracted. From the above extracted features, the optimized features used for the classification of breast cancer are identified using the cuckoo search optimization algorithm. Finally, the (SVM) classifier is trained using these optimized features, which in turn helps us to classify the breast cancer of type benign or malignant. The accuracy of the proposed system is validated using Mammographic Image Analysis Society (MIAS) public database images. The overall accuracy rate of our proposed system is about 96.72%, which is high enough when compared to the existing systems.