Abstract
Understanding the often-heterogeneous land cover in urban areas is critical for, among other things, environmental monitoring, spatial planning, and enforcement. Recently, several earth observation satellites were developed with an enhanced spatial resolution that provides for precise and detailed representations of image objects. Morphological image analysis techniques provide useful tools for extracting spatial features from high-resolution, remotely sensed images. This study investigated the efficacy of mathematical morphological (MM) techniques in the land cover classification of a heterogeneous urban landscape using very high-resolution pan-sharpened Pleiades imagery. Specifically, the study evaluated two morphological profiles (MP) techniques (i.e., concatenation of morphological profiles (CMPs) and multi-morphological profiles (MMPs)) in the classification of a heterogeneous urban land cover. The overall accuracies for CMP were 83.14% and 83.19% over the two study areas. Similarly, the MMP overall accuracies were 84.42% and 84.08% for the two study sites. The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes. In general, similar and visually acceptable results were produced for land cover classification using either CMP or MMP image analysis techniques
Highlights
Understanding the often-heterogeneous land cover in urban areas is critical for environmental management, urban spatial planning, and optimal and sustainable use of urban landscapes [1,2,3]
A STEP similarity matrix was used to determine the potential of multi-morphological profiles (MMPs) and concatenation of morphological profiles (CMPs) in discriminating a heterogeneous urban land cover
This study evaluated the efficacy of two morphological techniques, one based on the concatenation of morphological profile (MP) (CMP) and the other based on its MMP extension which used Principal component analysis (PCA) to reduce the dimensionality reduction problem associated with MP
Summary
Understanding the often-heterogeneous land cover in urban areas is critical for environmental management, urban spatial planning, and optimal and sustainable use of urban landscapes [1,2,3]. Methods used for urban land cover classification involve enumeration, observation, and field surveys. To increase the accuracy of urban landscapes, the recent proliferation of high spatial resolution sensors necessitates a further exploration of various techniques commonly classified into pixel- and object-based approaches. The efficacy of spectrally based pixel-based techniques is limited by landscape heterogeneity typified by urban areas and the ever-increasing suite of high spatial resolution imagery associated with recent sensor advancement. Object-based image analysis (OBIA) techniques were developed to exploit contextual information inherent in heterogeneous landscapes. Two distinct methods utilize spatial information from an image: region-based, such as the Grey Level Co-Occurrence Matrix (GLCM), and edge/window-based techniques, such as the Canny edge and Sobel operator. The major limitations related to edge-based techniques are insensitivity to noise and are edge-based, they are highly dependent on the analysis window which blurs the borders of textured regions [18,21]
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