Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network

Guo, Wei; Yang, Wen; Zhang, Haijian; Hua, Guang

doi:10.3390/rs10010131

Cited by 144 publications

(88 citation statements)

References 52 publications

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“…This dataset contains a total of 3775 object instances which are manually annotated with horizontal bounding boxes, including 757 airplanes, 390 baseball diamonds, 159 basketball courts, 124 bridges, 224 harbors, 163 ground track fields, 302 ships, 655 storage tanks, 524 tennis courts, and 477 vehicles. This dataset has been widely used in the earth observation community Cheng et al, 2018b;Farooq et al, 2017;Guo et al, 2018;Han et al, 2017a;Li et al, 2018;Yang et al, 2018b;Yang et al, 2017;Zhong et al, 2018). 4) VEDAI: The VEDAI (Razakarivony and Jurie, 2015) dataset is released for the task of multi-class vehicle detection in aerial images.…”

Section: Object Detection Datasets Of Optical Remote Sensing Imagesmentioning

confidence: 99%

Object detection in optical remote sensing images: A survey and a new benchmark

Wan

Cheng

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

1,098

425

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Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset contains 23463 images and 192472 instances, covering 20 object classes. The proposed DIOR dataset 1) is large-scale on the object categories, on the object instance number, and on the total image number; 2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter-and intra-class size variability across objects; 3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and 4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-theart approaches on our DIOR dataset to establish a baseline for future research.

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Section: Object Detection Datasets Of Optical Remote Sensing Imagesmentioning

confidence: 99%

Object detection in optical remote sensing images: A survey and a new benchmark

Wan

Cheng

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

1,098

425

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“…Our goal is to improve the spatial resolution of SWIR bands using a Convolutional Neural Network (CNN). CNNs have attracted an increasing interest in many remote sensing applications, like object detection [10], classification [11], pansharpening [12], and others, because of their capability to approximate complex non-linear functions, benefiting from the reduction in computation time obtained thanks to the GPU usage. On the downside the availability of a large amount of data is required for training.…”

Section: Proposed Cnn-based Super-resolution Fusionmentioning

confidence: 99%

A CNN-based Super-resolution Technique for Active Fire Detection on Sentinel-2 Data

Gargiulo

Dell'Aglio

Iodice

et al. 2019

2019 PhotonIcs &Amp; Electromagnetics Research Symposium - Spring (PIERS-Spring)

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Remote Sensing applications can benefit from a relatively fine spatial resolution multispectral (MS) images and a high revisit frequency ensured by the twin satellites Sentinel-2. Unfortunately, only four out of thirteen bands are provided at the highest resolution of 10 meters, and the others at 20 or 60 meters. For instance the Short-Wave Infrared (SWIR) bands, provided at 20 meters, are very useful to detect active fires. Aiming to a more detailed Active Fire Detection (AFD) maps, we propose a super-resolution data fusion method based on Convolutional Neural Network (CNN) to move towards the 10-m spatial resolution the SWIR bands. The proposed CNN-based solution achieves better results than alternative methods in terms of some accuracy metrics. Moreover we test the super-resolved bands from an application point of view by monitoring active fire through classic indices. Advantages and limits of our proposed approach are validated on specific geographical area (the mount Vesuvius, close to Naples) that was damaged by widespread fires during the summer of 2017.

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“…In order to verify the effectiveness of our proposed architecture, we use the most advanced object detection models based on in-depth learning tha have been used in recent years, i.e., R2-CNN [5], FPN Faster R-CNN [5], C-SPCL [47], F-RLN [48], MSCNN [8], DSSD [49], SDBD [50], YOLOv3 [51], VPN [7], MDSSD [52] as comparison algorithms, which are higher in DOTA, TGRS-HRD-Data. The results of multi-object detection are compared using the optical remote sensing image dataset of resolution.…”

Section: Comparison Experiments With State-of-the-art Modelsmentioning

confidence: 99%

Real-Time Object Detection in Remote Sensing Images Based on Visual Perception and Memory Reasoning

et al. 2019

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Aiming at the real-time detection of multiple objects and micro-objects in large-scene remote sensing images, a cascaded convolutional neural network real-time object-detection framework for remote sensing images is proposed, which integrates visual perception and convolutional memory network reasoning. The detection framework is composed of two fully convolutional networks, namely, the strengthened object self-attention pre-screening fully convolutional network (SOSA-FCN) and the object accurate detection fully convolutional network (AD-FCN). SOSA-FCN introduces a self-attention module to extract attention feature maps and constructs a depth feature pyramid to optimize the attention feature maps by combining convolutional long-term and short-term memory networks. It guides the acquisition of potential sub-regions of the object in the scene, reduces the computational complexity, and enhances the network’s ability to extract multi-scale object features. It adapts to the complex background and small object characteristics of a large-scene remote sensing image. In AD-FCN, the object mask and object orientation estimation layer are designed to achieve fine positioning of candidate frames. The performance of the proposed algorithm is compared with that of other advanced methods on NWPU_VHR-10, DOTA, UCAS-AOD, and other open datasets. The experimental results show that the proposed algorithm significantly improves the efficiency of object detection while ensuring detection accuracy and has high adaptability. It has extensive engineering application prospects.

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Geospatial Object Detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network

Cited by 144 publications

References 52 publications

Object detection in optical remote sensing images: A survey and a new benchmark

Object detection in optical remote sensing images: A survey and a new benchmark

A CNN-based Super-resolution Technique for Active Fire Detection on Sentinel-2 Data

Real-Time Object Detection in Remote Sensing Images Based on Visual Perception and Memory Reasoning

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