Mapping of built-up areas were always a main concern to researchers in the field of remote sensing. Thus, several techniques have been proposed to saving technicians from digitizing hundreds of areas by hand. Multiclass classifiers exhibit a very promising performance in terms of classification accuracy. However, they require that all classes in the study area to be labeled. In many applications, users may only be interested in a specific land class. This referred to as one-class classification (OC) problem. In this paper, we compare a Binary Support Vector Machine (BSVM) classifier, with two OC classifiers, OC SVM (OCSVM), and Presence and Background Learning (PBL) framework for the extracting built-up areas from Gaofen-2 and Aster satellites imagery. The obtained classification accuracies show that PBL provides competitive extraction results due to the fact that PBL is a positive-unlabeled method based on neural network in which large amounts of available unlabeled samples is incorporated into the training phase, allowing the classifier to model the built-up class more effectively.
Recently, the new Geographic object-based image analysis (GEOBIA) was proposed as an alternative classification approach to pixel based ones. In GEOBIA, image segments can be depicted with various attributes such as spectral, texture, shape, deep features and context, and hence final classification can produce better land cover/use map. The presence of such a large number of features poses significant challenges to standard machine learning methods and has rendered many existing classification techniques impractical. In this work, we are interested to feature selection techniques, which are employed to reduce the dimensionality of the data while keeping the most of its expressive power. Inspired by recent works in remote sensing using Convolutional Neural Networks (CNNs), especially for hyperspectral band selection, a feature selection approach based on One-Dimensional Convolutional Neural Networks (1-D CNN) is proposed in this study. All object-based features are used to train the 1-D CNN to obtain well trained model. After testing different feature combinations, we use the well trained model to obtain their test classification accuracies, and finally we select the subset of features with the highest precision. In our experiments, we evaluate our feature selection approach on 30-cm resolution colour infrared (CIR) aerial orthoimagery. A multi-resolution segmentation is performed to segment the images into regions, which are characterized later using various spectral, textural and spatial attributes to form the final object-based feature dataset. The obtained experimental results show that the proposed method can achieve satisfactory results when compared with traditional feature selection approaches.
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