Morphology-Combined Gradient Boosting for Recognizing Targets in SAR Images

Abstract

This article proposes a novel method for recognizing objects in synthetic aperture radar images. The target is initially detected using a proposed morphology-based segmentation process and is further confirmed by classifying the objects. The identified target after the proposed segmentation process is subjected to feature extraction using Zernike moments, which efficiently downsamples the features and makes them rotationally invariant. The features are classified using a tree-based method called gradient boosting. Gradient boosting, by far, has shown very promising results on various kinds of data sets. The main focus is to bring to light this kind of tree-based architecture for target recognition in satellite imagery as well as to propose a framework that reduces the overall time and improves the efficiency of the process. It outperforms the previous state-of-the-art methods for this data size. The proposed method is compared with existing techniques to measure and evaluate its performance.

Wood Species Recognition with Small Data: A Deep Learning Approach

“…RFC also contained in the Sklearn library and use the default parameters. Two efficient classification methods which are decision tree (DT) classifier and gradient boosting were compared in the experiments, these classifiers get a better result in the previous researches [31,32].…”