Land Use Classification With Engineered Features

Rasche, Christoph

doi:10.1109/lgrs.2020.3046308

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…The dataset includes only four types of objects: aircraft, oil drums, overpasses, and sports fields-a total of 976 images. (3) NWPU VHR-10 dataset [32][33][34][35][36]: The NWPU-RESISC45 dataset was proposed by researchers at Northwestern Polytechnical University, with a total of 45 categories and a total of 31,500 images. The experimental results were not entirely satisfactory due to the low resolution of the images.…”

Section: Datasetsmentioning

confidence: 99%

Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review

Wang

Song

et al. 2023

Remote Sensing

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With the accelerated development of artificial intelligence, remote-sensing image technologies have gained widespread attention in smart cities. In recent years, remote sensing object detection research has focused on detecting and counting small dense objects in large remote sensing scenes. Small object detection, as a branch of object detection, remains a significant challenge in research due to the image resolution, size, number, and orientation of objects, among other factors. This paper examines object detection based on deep learning and its applications for small object detection in remote sensing. This paper aims to provide readers with a thorough comprehension of the research objectives. Specifically, we aggregate the principal datasets and evaluation methods extensively employed in recent remote sensing object detection techniques. We also discuss the irregularity problem of remote sensing image object detection and overview the small object detection methods in remote sensing images. In addition, we select small target detection methods with excellent performance in recent years for experiments and analysis. Finally, the challenges and future work related to small object detection in remote sensing are highlighted.

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Section: Datasetsmentioning

confidence: 99%

Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review

Wang

Song

et al. 2023

Remote Sensing

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“…The images were extracted from USGS National Map Urban Area Imagery, and the spatial resolution is 0.3048 m. UC Merced is one of the most used datasets in remote sensing scene classification, and it has overlapping classes, such as sparsely residential, high-density residential, and medium-density residential areas, making this dataset richer and more challenging. Some of the remote sensing image classification studies include LGFBOVW classifier (Zhu et al, 2016), LASC-CNN classifier (Yuan et al, 2018), Hybrid Satellite Image Classification System (A. et al, 2018), Structured Metric Learning (Gong et al, 2019), mcODM classifier (Z. , Feature Engineering-based Classifier (Rasche, 2021).…”

Section: Object Detection Datasets For Rs Imagesmentioning

confidence: 99%

“…The researchers frequently used these datasets for remote sensing scene classification and object recognition tasks (Cheng et al, 2016;Dong et al, 2019;Rasche, 2021;C. Wang et al, 2019;Xu et al, 2020;Xue et al, 2020).…”

Section: Object Detection Datasets For Rs Imagesmentioning

confidence: 99%

Remote Sensing Super-resolution Object Detection (RSSOD) Dataset

2021

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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 spatial resolution of ~0.05 m. There are five classes with varying frequencies of labels per class; the images are annotated in YOLO and COCO format. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. The proposed RSSOD Preprint submitted to an Elsevier Journal for Review 2 dataset will help researchers benchmark the state-of-the-art object detection methods across various classes, especially for small objects using image super-resolution. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolutionbased object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2dB PSNR compared to the current state-of-the-art NLSN method. MCGR achieved best object detection mAPs of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet.

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Land Use Classification With Engineered Features

Cited by 2 publications

References 26 publications

Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review

Small Object Detection Based on Deep Learning for Remote Sensing: A Comprehensive Review

Remote Sensing Super-resolution Object Detection (RSSOD) Dataset

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