A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery

Zhang, Lu; Shi, Zhenwei; Wu, Jun

doi:10.1109/jstars.2015.2467377

Cited by 100 publications

(55 citation statements)

References 30 publications

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“…Zhou et al [99] propose a weakly supervised learning framework to train an object detector, where a pre-trained CNN model is transferred to extract high-level features of objects and the negative bootstrapping scheme is incorporated into the detector training process to provide faster convergence of the detector. Zhang et al [100] propose a hierarchical oil tank detector, which combines deep surrounding features, which are extracted from the pre-trained CNN model with local features (histogram of oriented gradients [101]). The candidate regions are selected by an ellipse and line segment detector.…”

Section: B Interpretation Of Sar Imagesmentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,318

1,204

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a "black-box" solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization. Following this wave of success and thanks to the increased availability of data and computational resources, the use of deep learning in remote sensing is finally taking off in remote sensing as well. Remote sensing data bring some new challenges for deep learning, since satellite image analysis raises some unique questions that translate into challenging new scientific questions:• Remote sensing data are often multi-modal, e.g. from optical (multi-and hyperspectral) and synthetic aperture radar (SAR) sensors, where both the imaging geometries and the content are completely different. Data and information fusion uses these complementary

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Section: B Interpretation Of Sar Imagesmentioning

confidence: 99%

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Zhu

Tuia

Mou

et al. 2017

IEEE Geosci. Remote Sens. Mag.

2,318

1,204

View full text Add to dashboard Cite

show abstract

“…Those methods often use pretrained CNN models on large data sets to handle the limited remote sensing training data. Zhang et al [31] used the trained CNN models to extract surrounding features. Those features were combined with features from HoG to get final representations and then applied gradient orientation to generate region proposals.…”

Section: Related Workmentioning

confidence: 99%

$\mathcal{R}^2$ -CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images

Pang

Xu³

et al. 2019

IEEE Trans. Geosci. Remote Sensing

214

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Recently, the convolutional neural network has brought impressive improvements for object detection. However, detecting tiny objects in large-scale remote sensing images still remains challenging. First, the extreme large input size makes the existing object detection solutions too slow for practical use. Second, the massive and complex backgrounds cause serious false alarms. Moreover, the ultratiny objects increase the difficulty of accurate detection. To tackle these problems, we propose a unified and self-reinforced network called remote sensing region-based convolutional neural network (R 2 -CNN), composing of backbone Tiny-Net, intermediate global attention block, and final classifier and detector. Tiny-Net is a lightweight residual structure, which enables fast and powerful features extraction from inputs. Global attention block is built upon Tiny-Net to inhibit false positives. Classifier is then used to predict the existence of targets in each patch, and detector is followed to locate them accurately if available. The classifier and detector are mutually reinforced with end-to-end training, which further speed up the process and avoid false alarms. Effectiveness of R 2 -CNN is validated on hundreds of GF-1 images and GF-2 images that are 18 000 × 18 192 pixels, 2.0-m resolution, and 27 620 × 29 200 pixels, 0.8m resolution, respectively. Specifically, we can process a GF-1 image in 29.4 s on Titian X just with single thread. According to our knowledge, no previous solution can detect the tiny object on such huge remote sensing images gracefully. We believe that it is a significant step toward practical real-time remote sensing systems.Index Terms-Object detection, remote sensing images, remote sensing region-based convolutional neural network(R 2 -CNN).

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“…Object detection plays a crucial role in image interpretation and also is very important for a wide scope of applications, such as intelligent monitoring, urban planning, precision agriculture, and geographic information system (GIS) updating. Driven by this requirement, significant efforts have been made in the past few years to develop a variety of methods for object detection in optical remote sensing images (Aksoy, 2014;Bai et al, 2014;Cheng et al, 2013a;Cheng and Han, 2016;Cheng et al, 2013b;Cheng et al, 2014;Cheng et al, 2019;Cheng et al, 2016a;Das et al, 2011;Han et al, 2015;Han et al, 2014;Li et al, 2018;Long et al, 2017;Tang et al, 2017b;Yang et al, 2017;Zhang et al, 2016;Zhang et al, 2017;Zhou et al, 2016).…”

Section: Introductionmentioning

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

1,082

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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A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery

Cited by 100 publications

References 30 publications

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

$\mathcal{R}^2$ -CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images

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

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