Deep ensemble model-based moving object detection and classification using SAR images - Paramasivam, Ramya; Kumar, Prashanth; Lai, Wen-Cheng; Bidare Divakarachari, Parameshachari (Frontiers in Earth Science, 2024)
In recent decades, the image processing and computer vision models plays a vital role in moving object detection on the Synthetic Aperture Radar (SAR) images. Capturing of moving objects in the SAR images is a difficult task. In this manuscript, a new automated model is proposed using SAR images for detecting moving objects. The proposed model has four main steps namely, preprocessing, segmentation, feature extraction, and classification. Initially, the input SAR image is pre-processed using a histogram equalization technique. Then, the weighted Otsu based segmentation algorithm is applied for segmenting the object regions from the pre-processed images. While using the weighted Otsu, the segmented grayscale images are not only clear but also retain the detailed features of grayscale images. Next, feature extraction is carried out by Gray Level Co-occurrence Matrix (GLCM), Median Binary Patterns (MBP) and Additive Harmonic Mean Estimated Local Gabor Binary Pattern (AHME-LGBP). The final step is classification using deep ensemble models, where the objects are classified by employing ensemble deep learning technique, combining the models like Bidirectional Long Short Time Memory (Bi-LSTM), Recurrent Neural Network (RNN), and Improved Deep Belief Network (IDBN), which is trained with the features extracted priory. The combined models increase the accuracy of the results significantly. Also, Ensemble modelling reduces the variance and modelling method bias which decreases the chances of overfitting. Compared to a single contributing model, an ensemble models perform better and make better predictions. Additionally, an ensemble lessens the spread or dispersion of the model performance and prediction accuracy. Finally, the performance of the proposed model is related to the conventional models with respect to different measures. In the mean case scenario, the proposed ensemble model has minimum error value of 0.032, which is better related to other models. In both median and best-case scenario studies, the ensemble model has lower error value of 0.029 and 0.015.