Abstract
Abstract. The classification problem in the image processing field is an important challenge, so that in the process image pixels are separated into previously determined classes according to their features. This process provides a meaningful knowledge about an area thanks to the satellite images. Satellite images are digital images obtained from a satellite vehicle by the way scanning the interest areas with some specified sensors. These sensors provide the specific radiometric and spatial information about the surface of the object. This information allows the researchers to obtain reliable classification results to be used to solve some real life problems such as object extraction, mapping, recognition, navigation and disaster management. Linear Discriminant Analysis (LDA) is a supervised method that reduces the dimensions of data in respect to the maximum discrimination of the elements of the data. This method also transfers the data to a new coordinate space in which the discriminant features of the classes are highest using the objection data provided manually. In this work, we consider the classes as if the satellite images have two classes; one is foreground and the other is background. The true classes such as roofs, roads, buildings, spaces and trees are treated sequentially as the foreground. The area outside the foreground class is treated as the background. The one dimensional reduced feature values of pixels, such that each value is reduced according to the binary classification of each class, are considered as membership values to the classes. In this way, each pixel has membership values for each of the classes. Finally, the pixels are classified according to the membership values. We used the ISPRS WG III/4 2D Semantic Labeling Benchmark (Vaihingen) images includes the ground truths and give the accuracy result values for each class.
Highlights
Classification of image pixels which are the smallest elements of images is an important problem in the satellite image processing field as in most computer vision fields, because obtaining meaningful information from satellite images is essential process for many remote sensing applications
We applied the developed method on the Vaihingen dataset provided by the ISPRS Commission III (‘2D Semantic Labeling Contest’, n.d.; Karakoyun et al, 2017; Labeling and Vaihingen, 2016)
The classifications of some satellite images obtained from a benchmark dataset are intended using one dimensional Linear Discriminant Analysis (LDA)
Summary
Classification of image pixels which are the smallest elements of images is an important problem in the satellite image processing field as in most computer vision fields, because obtaining meaningful information from satellite images is essential process for many remote sensing applications. Semantic segmentation has been an essential issue in satellite image processing, because this issue provides solutions for many ecological and socioeconomic problems (Vailaya et al, 1998) such as landslide monitoring, inferring geographical information, guidance information for intelligent military systems, infrastructure design, and disaster management (Montoya, 2003; Paisitkriangkrai et al, 2015). In remote sensing semantic segmentation, the pixels in the satellite images taken over an urban are generally labelled as road, building, tree, and vegetation. This task is a problem due to the fact that the pixels belonging to different classes may be similar to each other, and in the same way, some pixels belonging to the same class may be different from each other (Paisitkriangkrai et al, 2015). It is deduced from this case that if the pixels belonging to different classes have more distinguishing features, the classification would be better
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