Can I read Remote sensing image super-resolution and object detection: Benchmark and state of the art on EtoBox?

Remote sensing image super-resolution and object detection: Benchmark and state of the art by Yi Wang; Syed Muhammad Arsalan Bashir; Mahrukh Khan; Qudrat Ullah; Rui Wang; Yilin Song; Zhe Guo; Yilong Niu is a Computer Science article available to read on EtoBox.

What is Remote sensing image super-resolution and object detection: Benchmark and state of the art about?

## learning object detection MCGR A B S T R A C T 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 largescale, 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 spatial resolution of ~ 0.05 m. There are five classes with varying frequencies of labels per class; the images are annotated in You Only Look Once (YOLO) and Common Objects in Context (COCO) format. The image patches are extracted from satellite images, i

Who reads Remote sensing image super-resolution and object detection: Benchmark and state of the art?

Author

Yi Wang; Syed Muhammad Arsalan Bashir; Mahrukh Khan; Qudrat Ullah; Rui Wang; Yilin Song; Zhe Guo; Yilong Niu

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)