Several water index-based methods have been proposed in the literature, which, combine satellite multispectral bands in an algebraic expression. The objective of these water index-based methods is to increase the intensity contrast between water-pixels (surface water-body) and non-water pixels (built-up, soil, vegetation, etc.). The present investigation evaluates the Modified Normalized Difference Water Index (MNDWI) and the Automated Water Extraction Index (AWEI) using the Satellite data from Landsat 5 TM, Landsat 8 and Sentinel 2A at different time scenes. Based on visual inspection of the Lake Metztitlan water body mapping results, a high performance of AWEI approached via the OLI and the MSI sensors is observed. In the selected study area of [9210x9380]m, a statistical water pixel percentage of 30.703616% is observed in a flooding season and 9.884537% for a dry season of the year.
Urban growth, deforestation, water resources and thawing of the poles due to globe worming are topics of interest in the research community. Normalize difference indices are utilized in remote sensing to analyzed and classify surface cover types. In this paper research, a multispectral satellite data from Landsat 5 TM is preprocessing, in order to addresses and evaluates accuracy of Normalized difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI) and Normalized Difference Snow Index (NDSI) at different time scenes. A quantitative statistical pixel percentages of build-up, vegetation cover, snow/ice and water body is given in this study for different periods of time.
Nowadays, breast lesions are a common health problem among women. Breast thermograms are images recorded by digital-optical systems with high resolution that use infrared technology in order to show vascular and temperature changes. In the present work, we study benign and malignant breast lesions shape by means of fractal analysis. The Fractal Dimension (FD) is calculated with the Box Counting method and the Hurst exponent is obtained using the Wavelet coefficients and the Detrending Moving Average algorithm. These algorithms was applied to synthetic images and breast thermograms. The Fractal Dimension value is used for patient classification with or without breast injury. The proposed methodology was applied to the Database For Mastology Research (DMR) in order to classify thermographic images. The FD of ROIs for breast thermograms was calculated. Results shows that the FD BCM values ranges from [0.45,0.81] in 4 healthy cases and from [0.92,1.33] in 4 unhealthy cases.