Single Image Based Algal Bloom Detection Using Water Body Extraction and Probabilistic Algae Indices

Abstract

Algal blooms are collections of algae that exist on the surface of the water. Because of their negative effects on aquatic organisms and humans, extensive studies have been performed to detect harmful algal blooms (HABs). However, most of the detection methods are based on remote-sensing imaging and have limitations with regard to resolution, time, and cost. In this paper, we present a new cyanobacterial algal bloom detection algorithm in inland water from a single image. The proposed method can be used as a first step in automatic early detection, warning, and rapid response systems that can be employed to mitigate the detrimental effects of HAB contamination in inland water bodies. We first divide an image into homogeneous regions via a density-based spatial clustering (DBSCAN) algorithm. From the segmented regions, we extract water bodies using wavelet leader-based texture analysis. The entropy and the number of zero wavelet coefficients are used as measures for the water body extraction. For images with a sky region, we introduce a simple sky-region removal method using the average brightness of segmented regions. We propose three probabilistic indices bases on an RGB-based vegetation index, a hue-based index, and a saturation-based index for estimating the degree of green algae in the extracted water body. The final index is obtained via multiplication of these three indices. In experiments on various types of images, our proposed algorithm achieves 94% accuracy for water body extraction. The proposed approach achieves better green algae estimation performance than the conventional vegetation index-based methods.