9 research outputs found
Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications
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A Band Selection Method For Hyperspectral Image Classification Based On Cuckoo Search Algorithm With Correlation Based Initialization
No full textAn Optimized Breast Cancer Diagnosis System Using a Cuckoo Search Algorithm and Support Vector Machine Classifier
No full textALST-W integrated index for enhanced surface temperature mapping of water bodies and vegetation using Landsat 8/9 satellite bands
No full textAbstract 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
Effect of solvent on the morphology of MoS2 nanosheets prepared by ultrasonication-assisted exfoliation
No full textStrategies for dimensionality reduction in hyperspectral remote sensing: A comprehensive overview
No full textThe technological advancements in spectroscopy give rise to acquiring data about different materials on earth's surface which can be utilized in a variety of potential applications. But, the hundreds of spectral bands are generally equipped with highly correlated information with limited training samples. This will degrade the Hyperspectral Image (HSI) classification accuracy. So Dimensionality Reduction (DR) has become inevitable and necessary step need to incorporate before HSI classification. The main contribution of this work lies in comparative study and review on dimensionality reduction techniques for Hyperspectral remote sensing image classification. The related challenges and research directions are also discussed. This study will help the researchers in the Hyperspectral remote sensing community to choose the appropriate DR technique for classification which can be useful in various real time applications
Multi-objective multi-verse optimizer based unsupervised band selection for hyperspectral image classification
No full text39904024Hyperspectral band selection is one of the efficacious ways to diminish the size of hyperspectral images. The process of selecting a few useful bands will be successful when two fundamental aspects are considered: information abundance and redundancy among the chosen bands. However, selecting the suitable number of bands in an ill-posed classification problem remains challenging. Overcoming this issue, a novel unsupervised multi-objective multi-verse optimizer-based band selection (MOMVOBS) approach is proposed. It explores optimal trade-offs among the different traits of the objective functions namely information richness, less redundancy and the number of bands to be selected. These three objective functions are optimized simultaneously using a multiverse optimizer (MVO) to obtain the best solutions. To evaluate the quality of selected bands, two widely used supervised classifiers are used, such as support vector machine (SVM) and K-nearest neighbour (KNN). Experimental results evidence for the superiority of the proposed approach over the recent multi-objective optimization-based band selection approaches by selecting the highly informative distinct bands that have better classification performance on four benchmark hyperspectral data sets. The proposed MOMVOBS have obtained 79.50% and 71.35% of overall accuracy for SVM and KNN classifier, respectively, in Indian Pines dataset with 10% of band retention, 93.06% and 88.88% of overall accuracy for SVM and KNN classifier, respectively, in Salinas dataset with 10% of band retention, 92.86% and 85.35% of overall accuracy for SVM and KNN classifier, respectively, in Pavia University dataset with 15% band retention, and 92.42% and 85.33% of overall accuracy for SVM and KNN classifier, respectively, in Botswana dataset with 11% band retention. The achievement of higher accuracy at less than 15% bands is significant.431
Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
No full textHyperspectral 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
