Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis

Abstract

Crude oil spills have negative consequences on the economy, environment, health and society in which they occur, and the severity of the consequences depends on how quickly these spills are detected once they begin. Several methods have been employed for spill detection, including real time remote surveillance by flying aircrafts with surveillance teams. Other methods employ various sensors, including visible sensors. This paper presents an algorithm to automatically detect the presence of crude oil spills in images acquired using visible light sensors. Images of crude oil spills used in the development of the algorithm were obtained from the Shell Petroleum Development Company (SPDC) Nigeria website The major steps of the detection algorithm are image preprocessing, crude oil color segmentation, sky elimination segmentation, Region of Interest (ROI) extraction, ROI texture feature extraction, and ROI texture feature analysis and classification. The algorithm was developed using 25 sample images containing crude oil spills and demonstrated a sensitivity of 92% and an FPI of 1.43. The algorithm was further tested on a set of 56 case images and demonstrated a sensitivity of 82% and an FPI of 0.66. This algorithm can be incorporated into spill detection systems that utilize visible sensors for early detection of crude oil spills.

Formation and Schema Analysis of Oil Painting Style Based on Texture and Color Texture Features under Few Shot

“…Modeling is not a single visual grasp, simple object reproduction. Graphic appearance writing is the active pursuit of perceptual psychology and an activity of the interaction between subject consciousness and object spiritual essence [ 15 ]. The establishment and improvement of the modeling system is a sign of the maturity of a painter.…”

Oil spill identification in visible sensor imaging using automated cross correlation with crude oil image filters

“…Fifty (50) images of documented crude oil spills in the Niger Delta region of Nigeria in 2019 by SPDC were obtained from SPDC [19]. Image pre-processing was done to remove the label pixels at the bottom of each image [1]. Next, each crude oil filter was cross-correlated with each crude oil filter in three phases: colour cross correlation, homogeneity cross correlation, and PSD cross correlation.…”

“…Crude oil spills are responsible for the destruction of soil microorganisms and agricultural crops. This leads to severe reduction in fishing and farming activities and results in poverty and regional underdevelopment [1]. The public expectation is that oil spill extent and locations are precisely mapped to implement countermeasures to minimize the effect of pollution [10].…”

A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater

“…The negative consequences of spills have made of them a growing concern for society and, therefore, different control and removal methods have been developed to minimize their impact on the environment [ 13 , 14 ]. The first stage of spill control consists of containment and recovery, followed by strategies that focus on shoreline cleaning and elimination of any remaining oil/fuel [ 15 ].…”