Object detection using adaptive mask RCNN in optical remote sensing images

Abstract

Fast and automatic object detection in remote sensing images is a critical and challenging task for civilian and military applications. Recently, deep learning approaches were introduced to overcome the limitation of traditional object detection methods. In this paper, adaptive mask Region-based Convolutional Network (mask-RCNN) is utilized for multi-class object detection in remote sensing images. Transfer learning, data augmentation, and fine-tuning were adopted to overcome objects scale variability, small size, the density of objects, and the scarcity of annotated remote sensing image. Also, five optimization methods were investigated namely: Adaptive Moment Estimation (Adam), stochastic gradient decent (SGD), adaptive learning rate method (Adelta), Root Mean Square Propagation (RMSprop) and hybrid optimization. In hybrid optimization, the training process begins Adam then switches to SGD when appropriate and vice versa. Also, the behaviour of adaptive mask RCNN was compared to baseline deep object detection methods. Several experiments were conducted on the challenging NWPU-VHR-10 dataset. The hybrid method Adam_SGD acheived the highest Accuracy precision, with 95%. Experimental results showed detection performance in terms of accuracy and intersection over union (IOU) boost of performance up to 6%.