Vision-based texture and color analysis of waterbody images using computer vision and deep learning techniques

Abstract

AbstractVision-based analysis of waterbodies can provide important information required for monitoring, analyzing, and managing water resource systems, such as visual flood detection, delineation, and mapping. Water, however, is an ornery object in image processing, as it can be found in different forms and colors in nature. This makes the detection, classification, and tracking of water in images and videos difficult for computer vision models. There are still visual differences resulting from water texture and its inherent optical properties associated with different waterbodies which can be recognized and extracted to support computer models to better analyze water images. This study aims to utilize a set of early, mid-level, and high-level vision techniques, including Gabor kernels, local binary patterns (LBPs), and deep learning (DL) models to extract and analyze water texture and color of different waterbodies in digital images. For this purpose, ATLANTIS TeXture (ATeX), an image dataset for waterbodies classification and texture analysis, was used. Models were trained for the task of classification on ATeX. Then, the performance of each model in extracting texture features was evaluated and compared. Results showed that the classification accuracy achieved by the Gabor magnitude tensor, LBP, and DL model (ShuffleNet V2 × 1.0) are 29, 35, and 92%, respectively, and thus the DL model outperforms traditional vision-based techniques. Moreover, the classification results on raw images represented by different color spaces (e.g., RGB, HSV, etc.) emphasized the importance of color information for digital image processing of water. Analyzing representative visual features and properties of different water types and waterbodies can facilitate designing a customized Convolutional Neural Networks (CNNs) for water scenes, as CNNs recognize objects through the analysis of both texture and shape clues and their relationship in the entire field of view.