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• W4295855144 abstract "Currently, large quantities of remote sensing images with different resolutions are available for earth observation and land monitoring, which are inevitably demanding intelligent analysis techniques for accurately identifying and classifying land use (LU). This article proposes an adaptive multi-scale superpixel embedding convolutional neural network architecture (AMUSE-CNN) for tackling land use classification. Initially, the images are parsed via the superpixel representation so that the object based analysis (via a superpixel embedding CNN scheme) can be carried out with the pixel context and neighborhood information. Then, a multi-scale convolutional neural network (MS-CNN) is proposed to classify the superpixel based images by identifying object features across a variety of scales simultaneously in which multiple window sizes are used to fit to the various geometries of different LU classes. Furthermore, a proposed adaptive strategy is applied to best exert the classification capability of MS-CNN. Subsequently two modules are developed to fully implement the AMUSE-CNN architecture. More specifically, Module I is to determine the most suitable classes for each window size (scale) by applying majority voting to a series of MS-CNNs. Module II carries out the classification of the classes identified in Module I for the given scale used in MS-CNN and therefore complete the LU classification of the entire classes. The proposed AMUSE-CNN architecture is both quantitatively and qualitatively validated using remote sensing data collected from two cities, Kano and Lagos in Nigeria due to the spatially complex land use distribution. Experimental results show the superior performance of our approach against several state-of-the-art techniques." @default.
• W4295855144 created "2022-09-15" @default.
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• W4295855144 date "2022-01-01" @default.
• W4295855144 modified "2023-09-26" @default.
• W4295855144 title "Adaptive Multiscale Superpixel Embedding Convolutional Neural Network for Land Use Classification" @default.
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• W4295855144 doi "https://doi.org/10.1109/jstars.2022.3203234" @default.
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