Integrated NIR-HE based SPOT-5 image enhancement method for features preservation and edge detection

Abstract

Recently, many researchers have directed their attention to methods of predicting shorelines by the use of multispectral images. Thus, a simple and optimised method using image enhancements is proposed to improve the low contrast of the Satellite pour l'Observation de la Terre-5 (SPOT-5) images in the detection of shorelines. The near-infrared (NIR) channel is important in this study to ensure the contrast of the vegetated area and sea classification, due to the high reflectance of leaves in the near infrared wavelength region. This study used five scenes of interest to show the different results in shoreline detection. The results demonstrated that the proposed method performed in an enhanced manner as compared to current methods when dealing with the low contrast ratio of SPOT-5 images. As a result, by utilising the near-infrared histogram equalization (NIR-HE), the contrast of all datasets was efficiently restored, producing a higher efficiency in edge detection, and achieving higher overall accuracy. The improved filtering method showed significantly better shoreline detection results than the other filter methods. It was concluded that this method would be useful for detecting and monitoring the shoreline edge in Tanjung Piai.

Two-stages of segmentation to improve accuracy of deep learning models based on dairy cow morphology

“…The steps in Canny edge detection are very important in digital image processing [26] and it is the first step in pattern recognition [27] as well as segmentation. This edge detection consists of taking the dairy cow image edges in order to retrieve useful information from the image [28]. The steps for edge detection include: i) noise reduction, which deals with smoothing the image using a Gaussian blur filter; ii) gradient calculation for producing two pieces of information from the image, such as the edge strength or its magnitude and the edge direction or orientation [29]; iii) non-maximum suppression, which is the stage of producing a slimmer thin-line using the orientation value in order to determine the pixel's direction; and iv) thresholding is the final step of the Canny algorithm, which is to perform hysteresis thresholds.…”