Abstract
Synthetic aperture radar (SAR) has become one of the most important means of information acquisition in today’s society and shows great potential in many fields. Target identification and classification of SAR images are also the focus of research. With the vigorous development of deep learning, many researchers apply this method to SAR target classification to obtain a more automatic process and more accurate results. In this paper, a novel deep forest model constructed by multi-grained cascade forest (gcForest), which is different from the traditional neural network (NN) model, is employed to classify ten types of SAR targets in the moving and stationary target acquisition and recognition (MSTAR) dataset. Considering that the targets of input images may be off-center and of different sizes in practical applications, two improved models based on varying weights by image features have been put forward, and both obtain better results. A series of experiments have been conducted to optimize model parameters, and final results with the MSTAR dataset illustrate that the two improved models are both superior to the original gcForest model. This is the first attempt to classify SAR targets using the non-NN model.
Highlights
As an active microwave remote-sensing device, synthetic aperture radar (SAR) is capable of providing high-resolution images independent of weather conditions and sunlight illumination, and is of great application value in military reconnaissance, environmental monitoring, geological exploration, disaster prediction, and other fields [1,2,3,4,5]
ATRheimsaamgpelewnausmcoblelersctoefdeaatch15taprgitecthtyapnegilnest,hae ddiaftfaesreetnacereodfiff2epreitncth aanndglaersetlhisattecdaninbTeacbolens2i.deTrheed inneitgilailgsibizlee. oTfhseasmapmlpesleisnu12m8b*er1s28o,f aenadchthtaerngeimt taygpeesianrethceudt aintatsoestiazree 9d6if*fe9r6etnot raendduacerethliestaemd oinunTtabolfec2o.mTphuetiantiitoinal. size of samples is 128 * 128, and images are cut into size 96 * 96 to reduce the amount of computation
A novel deep forest method implemented by the gcForest is used to explore the field of SAR target classification
Summary
As an active microwave remote-sensing device, synthetic aperture radar (SAR) is capable of providing high-resolution images independent of weather conditions and sunlight illumination, and is of great application value in military reconnaissance, environmental monitoring, geological exploration, disaster prediction, and other fields [1,2,3,4,5]. High-resolution SAR images contain a wealth of detailed information, which can immensely widen its application areas [6,7,8,9]; this makes SAR image interpretation extremely complicated. With the development of deep learning, increasingly intelligent methods have been applied to SAR image interpretation. Multi-grained cascade forest (gcForest) is an approach to construct a deep forest [32]. It is a novel ensemble method with a cascade decision tree structure, which can enhance its representation learning ability. The deep forest is made up of two parts, multi-grained scanning and cascade forest, which will be introduced in the following part. The original d*d image is partitioned into n*n panes of m*m size, in which m is the side length of a sliding window and n can be computed as follows: n
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
