LR-TSDet: Towards Tiny Ship Detection in Low-Resolution Remote Sensing Images

Yuxin Hu,Jixiang Wu,Bin Lei,Zongxu Pan

doi:10.3390/rs13193890

Abstract

Recently, deep learning-based methods have made great improvements in object detection in remote sensing images (RSIs). However, detecting tiny objects in low-resolution images is still challenging. The features of these objects are not distinguishable enough due to their tiny size and confusing backgrounds and can be easily lost as the network deepens or downsamples. To address these issues, we propose an effective Tiny Ship Detector for Low-Resolution RSIs, abbreviated as LR-TSDet, consisting of three key components: a filtered feature aggregation (FFA) module, a hierarchical-atrous spatial pyramid (HASP) module, and an IoU-Joint loss. The FFA module captures long-range dependencies by calculating the similarity matrix so as to strengthen the responses of instances. The HASP module obtains deep semantic information while maintaining the resolution of feature maps by aggregating four parallel hierarchical-atrous convolution blocks of different dilation rates. The IoU-Joint loss is proposed to alleviate the inconsistency between classification and regression tasks, and guides the network to focus on samples that have both high localization accuracy and high confidence. Furthermore, we introduce a new dataset called GF1-LRSD collected from the Gaofen–1 satellite for tiny ship detection in low-resolution RSIs. The resolution of images is 16m and the mean size of objects is about 10.9 pixels, which are much smaller than public RSI datasets. Extensive experiments on GF1-LRSD and DOTA-Ship show that our method outperforms several competitors, proving its effectiveness and generality.

Highlights

IntroductionObject detection [1,2,3] in remote sensing images (RSIs) aims to locate objects of interest (e.g., ships [4,5], airplanes [6,7] and storage tanks [8,9]) and identify corresponding categories, playing an important role in urban planning, automatic monitoring, geographic information system (GIS) updating, etc
We propose the filtered feature aggregation (FFA) module to make use of complex backgrounds, which can be plugged into the feature pyramid network (FPN) [33]
In the RetinaNet-D design, {P6, P7} in FPN are obtained by strided convolutions. This is done with the aim of improving large object detection, which may miss tiny object information and generate many unmatched negative samples that adversely affect the network training; our experiments proved this

Summary

Introduction

Object detection [1,2,3] in remote sensing images (RSIs) aims to locate objects of interest (e.g., ships [4,5], airplanes [6,7] and storage tanks [8,9]) and identify corresponding categories, playing an important role in urban planning, automatic monitoring, geographic information system (GIS) updating, etc. The very high-resolution (VHR) RSIs provide abundant spatial and textural information regarding their targets, and are widely used in target extraction and recognition [10], landcover classification [11], etc. The low-resolution RSIs tend to have a large field of view and contain more targets than VHR images of the same size, attracting much attention in object detection [4,12,13] and tracking [14] tasks

Methods

Results

Conclusion