Modified Deep Reinforcement Learning with Efficient Convolution Feature for Small Target Detection in VHR Remote Sensing Imagery

Shuai Liu,Jialan Tang

doi:10.3390/ijgi10030170

Abstract

Small object detection in very-high-resolution (VHR) optical remote sensing images is a fundamental but challenaging problem due to the latent complexities. To tackle this problem, the MdrlEcf model is proposed by modifying deep reinforcement learning (DRL) and extracting the efficient convolution feature. Firstly, an efficient attention network is constructed by introducing the local attention into the convolutional neural network. Combining the shallow low-level features with rich detail descriptions and high-level features with more semantic meanings effectively, efficient convolution features can be obtained. By this, the attention network can effectively enhance the ability to extract small target features and suppressing useless features. Secondly, the efficient feature map is sent to the region proposal network constructed by modified DRL. Using the modified reward function, this model can accumulate more rewards to conduct the search process, and potentially generate effective subsequent proposals and classification scores. It also can increase the effectiveness of object locations and classifications for small targets. Quantitative and qualitative experiments are conducted to verify the detection performance of different models. The results show that the proposed MdrlEcf can effectively and accurately locate and identify related small objects.

Highlights

The VHR remote sensing imagery (RSI) develops quickly due to the wide exploration of sensor technologies and aerospace research
The following descriptions of the SE module can apply to the local attention, just replace the SE module with the local attention
Analyzing the Average Precision (AP) values of each category, we found that SSD and YOLO show better results in some categories, but their overall evaluation is not as good as MdrlEcf

Summary

Introduction

The VHR remote sensing imagery (RSI) develops quickly due to the wide exploration of sensor technologies and aerospace research. Its typical resolution is 3–4-m ground sample distance (GSD) and the objects in VHR images are usually of diverse shapes in arbitrary orientations. With the advantages of large-scale images and multi-angle data, the VHR remote sensing images have supported an increasingly wide range of applications including resource exploration, urban planning, natural disaster assessment, military target detection, and recognition. Different from natural images, the objects in VHR RSI such as cars have a relatively smaller spatial extent (usually smaller than 15 pixels [3]) than the other large satellite objects. The much smaller objects [4] and complex background content [5] greatly limit its detection performance and pose some severe challenges for the applications [6,7]

Methods

Results

Conclusion