Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping

Zhou, Peicheng; Cheng, Gong; Liu, Zhenbao; Bu, Shuhui; Hu, Xintao

doi:10.1007/s11045-015-0370-3

Cited by 87 publications

(31 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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]).…”

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.

2,442

1,204

View full text Add to dashboard Cite

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

show abstract

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.

2,442

1,204

View full text Add to dashboard Cite

show abstract

“…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

Self Cite

1,248

425

View full text Add to dashboard Cite

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.

show abstract

“…Several studies have been conducted on the automatic identification of different targets, such as buildings, aircraft, ships, etc., to reduce human-induced errors and save time and effort [1,3,4]. However, the complexity of the background; differences in data acquisition geometry, topography, and illumination conditions; and the diversity of objects make automatic detection challenging for satellite images.…”

Section: Introductionmentioning

confidence: 99%

Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images

2020

View full text Add to dashboard Cite

Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large-and medium-sized airplanes were detected with higher accuracy.

show abstract

Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping

Cited by 87 publications

References 44 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

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

Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images

Contact Info

Product

Resources

About