Efficacy of morphological approach in the classification of urban land covers.
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Date
2020
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Abstract
Understanding the often-heterogeneous land use land cover (LULC) in urban areas is critical for among
others environmental monitoring, spatial planning and enforcement. Recently, several earth observation
satellites have been developed with enhanced spatial resolution that provide for precise and detailed
representation of image objects. This has generated new demand for enhanced processing capabilities.
Thus, the need for techniques that incorporate spatial and spectral information in the analysis of urban
LULC has drawn increasing attention. Enhanced spatial resolution comes with challenges for most pixel
based classifiers. This include salt and pepper effects that arise from incapability of pixel based
techniques in considering spatial or contextual information related to the pixel of interest during image
analysis. These challenges have often contributed to the inaccuracy of heterogeneous LULC
classification. Object based techniques on the other hand have been proposed to provide effective
framework for incorporating spatial information in their analysis. However, challenges such as
over/under segmentation and difficulty or non-robust statistical estimation hamper most object
techniques in achieving optimum performance. Thus, to achieve optimum LULC classification, the full
exploitation of both spectral-spatial information is essential. Hence, this study investigated the efficacy
of Mathematical Morphological (MM) techniques referred to as morphological profiles (MP) in LULC
classification of a heterogeneous urban landscape. The first objective of the study evaluated two MP
techniques i.e. concatenation of morphological profiles (CMP) and multi-morphological profiles
(MMP) in the classification of a heterogeneous urban LULC. Findings from this study indicated that
both CMP and MMP provided higher accuracies in classifying a heterogeneous urban landscape.
However, in evaluating their capability in preserving geometrical characteristics such as shape, theme,
edge and positional similarity of image structures, CMP provided higher accuracies than MMP. This
was attributed to the use of Principal Component Analysis (PCA) in the construction of MMP that
resulted in the distorted edges of some of the image objects. However, in comparing the techniques in
terms of the capability to discriminate image objects, MMP provided higher classification accuracies
compared to CMP. This can be attributed to the former’s capability to exploit both spectral and spatial
information from very high spatial resolution imagery. Hence in the second objective, MMP was
adopted due to its ability to deal with dimensionality problem associated with CMP and its superior
object discrimination capability. The findings indicated that MMP significantly enhanced ML and SVM
classification accuracies. Specifically, the use of MMP as a feature vector for SVM and ML
classification increased LULC distinction of objects with similar spectral signatures in a heterogeneous
urban landscape. This is due to its capability to provide an effective framework for synthesis of spectral
and spatial information. Overall the study demonstrated that morphological techniques provides robust
novel image analysis techniques which can effectively be used for operational classification of a
heterogeneous urban LULC.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.