An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery

Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products.

Performance Analysis on the Effect of Noise in Inverse Surface Adaptive Thresholding (ISAT)

Thresholding is one of the powerful methods in segmentation phase. Numerous methods were proposed to segment the foreground from the background but there is limited number of studies that analyse the effect of noise since the present of noise will affect the performance of the thresholding method. In this paper, the main idea is to analyse the effect of noise in Inverse Surface Adaptive Thresholding (ISAT) method. ISAT method is known as an excellent method to segment the image with the present of noise. The result of this analysis can be a guideline to researcher when implementing ISAT method especially in medical image diagnosis. Initially, several images with different noise variations were prepared and underwent ISAT method. In ISAT method, several image processing methods were incorporated namely edge detection, Otsu thresholding and inverse surface construction. The resulting images were evaluated using Misclassification Error (ME) to evaluate the performance of the segmentation result. Based on the obtained results, ISAT performance is consistent although the noise percentage increases from 5% to 25%.

Binarization of Ancient Malayalam Documents - A Novel Weight-based Denoising Approach

Even though several studies exist on denoising degraded documents, now a days it is a tedious task in the field of document image processing because ancient document may contain several degradations which will be a barrier for reader. Here we use old Malayalam Grantha scripts that contain useful information like the poem titled ‘Njana Stuthi’ and ancient literature. These historical documents are losing content due to heavy degradations such as, ink bleed, fungi-found to be brittleness & show through. In order to remove these kind of degradations, the study is proposing a novel binarization algorithm which remove noises from Grantha scripts as well as notebook images and make the document readable. Here we use 500 datasets of Grantha scripts for experimentation. In our proposed method, binarization is done through a channel based method in which we are converting image in to RGB, further adding weights to make the image darker or brighter followed by morphological operation open and finally passing it RGB and HSV channel for more clarity and clear separation of black text and white background, remaining noise will be removed using adaptive thresholding technique. The proposed method is outperformed with good accuracy.