Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art
Yi Wang, Syed Muhammad Arsalan Bashir, Mahrukh Khan, Qudrat Ullah, Rui, Wang, Yilin Song, Zhe Guo, Yilong Niu

TL;DR
This paper introduces a new benchmark dataset for remote sensing image super-resolution and object detection, proposes a novel super-resolution GAN, and demonstrates improved detection performance over existing methods.
Contribution
It provides a large-scale, annotated dataset (RSSOD), a new super-resolution model (MCGR), and benchmarks object detection performance in high-resolution remote sensing images.
Findings
MCGR achieved state-of-the-art PSNR of 1.2dB higher than NLSN.
MCGR outperformed existing detectors with mAPs up to 0.983.
The RSSOD dataset includes diverse, high-resolution satellite images with real distortions.
Abstract
For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a large-scale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high resolution (VHR) images with a…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Image Fusion Techniques · Remote-Sensing Image Classification
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Feature Pyramid Network · Pointwise Convolution · Batch Normalization · Depthwise Convolution · Non Maximum Suppression · 1x1 Convolution · Depthwise Separable Convolution · Convolution · Focal Loss
