Aeroplane Detection Techniques in Optical Satellite Sensors

Aeroplane Detection Techniques in Optical Satellite Sensors

DOI: 10.4018/978-1-7998-1920-2.ch008


This chapter demonstrates an automatic detection approach for aeroplanes in optical satellite data. This chapter hypothesizes that aeroplane fuselage can be retrieved in satellite images. Aeroplane detection is a challenging task in remote sensing images due to its variable sizes, colours, complex backgrounds, and orientations. To this end, principle component analysis (PCA) and a deep belief network (DBN) are used to detect the MH370 flight. Needless to say that all detected targets are not segments of MH370.
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General Procedures For Detection Of Aeroplane In Satellite Data

In high-resolution satellite images, aeroplane recognition is proved to be a challenging task because of its multifaceted structure, variable dimensions, colours, and orientations (Deepa and Kala 2017). In this view, geometrical shape, image background, and image gradient across the aeroplane are such parameters, which impact the detection of the aeroplane through an image processing tool.

The main approach for aeroplane detection is a function of the aeroplane shape features. This method is considered extremely idealistic for object detection in remote sensing data. On the other hand, this approach is controlled by different aeroplane shape types. In this context, this approach must be based on the specific templates, which involve physical characteristics for each sort of aeroplane. In this regard, it can be easy to match the detected object to the different template kinds (Vaijayanthi and Vanitha, 2015). In this procedure, the similarity between objects does not rely on the overall shape detection. Consequently, this approach can detect aeroplane persistently deprived of impeccable abstraction of the frame or shape of targets in the function of a circumstance. For instance, it relies on the circumstance of parts missing and shadow disorder.

Generally, the recognition approach involves: (i) possibly refocusing targets are primarily notorious on time-series satellite images; (ii) the object is then retrieved by computing both spectral and spatial features; and (iii) lastly, matching procedure uses to distinguish an aeroplane for precise detection (Figure 1). Moreover, there are also other identification procedures, which is based on computing the direction post binarization (Wuet al., 2015), and then classify the aeroplane kind. Nonetheless, these procedures also require the binary satellite images of each aeroplane kind as a prerequisite for direction evaluation, which reduces the feasibility. Moreover, aeroplane recognition often experiences numerous disorders, for example, dissimilar contrasts, clutter, and homogeneity strength (Dudani e al., 1997).

Figure 1.

Identification procedures of an aeroplane in satellite images


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