A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region

Abstract

A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.

Evaluating Land Use Land Cover Classification Based on Machine Learning Algorithms

“…The first step in supervised classification is identifying the regions used as simulated training locations for the various land cover classifications [5]. A method known as supervised classification is one in which the user works as a supervisor for pixel categorization.…”

“…Comprehending LULC changes is essential in many sectors that rely on Earth data, including factors like local and regional planning [3,4]. The accurate classification techniques are based on a sensor, spatial and spectral resolutions, and optical or active rolling environmental changes utilizing optical and microwave imaging from various sensors, and the approaches take into account ecological changes over time [5]. Land use research initiatives worldwide have been essential to international research on climate and environmental change [6].…”

Cálculo da curva número para bacia hidrográfica urbana utilizando diferentes abordagens de classificação para imagem orbital RapidEye: estudo de caso para o arroio Pepino (Pelotas, RS)

“…O algoritmo MaxVer avalia a variância e covariância das categorias dos padrões de resposta espectral para a classificação de pixels desconhecidos, assumindo-se que a distribuição dos valores dos pixels em cada banda nas classes é Gaussiana (distribuição normal). A partir desta suposição, calcula-se a probabilidade de um dado pixel pertencer a uma classe específica (Omo-Irabor, 2016;Wang et al, 2018). O pixel será classificado de acordo com a classe que apresentar maior probabilidade de pertencimento.…”

A new land-cover match-based change detection for hyperspectral imagery

“…This small window of spectral bands is one of the primary disadvantages of MS sensors, because some of the observed objects show the same spectral behavior as other, different objects when using the coarse bandwidths of current MS sensors (Govender, Chetty, Naiken, & Bulcock, 2008;S. Liu, 2015;Omo-Irabor, 2016). This in turn affects the ability to identify and distinguish objects accurately, lowering overall accuracy (OA) and making the spectral information offered by MS data less elaborate.…”

“…It offers more abundant and more detailed information and can distinguish objects that would otherwise appear similar. Aside from providing increased identification power, the higher resolution further increases accuracy by providing more details, thus promoting efficiency in most applications, especially in CD analysis (S. Liu, 2015;Omo-Irabor, 2016;Smith, 2012;C. Wu et al, 2013).…”