BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

Sun, Xian; Shi, Aijun; Huang, Hai; Mayer, Helmut

doi:10.1109/jstars.2020.3021098

Cited by 94 publications

(42 citation statements)

References 46 publications

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“…Semi-supervised learning [149] Utilize labeled and unlabeled input images to train ResNet, and a discriminator is also used as an auxiliary network for training.…”

Section: Semantic Segmentation Related To Dl-based Lummentioning

confidence: 99%

Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review

Zang

Cao

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Land-use mapping (LUM) using high spatial resolution remote sensing images (HSR-RSIs) is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs, such as different image acquisition conditions and massive, detailed information, performing LUM faces unique scientific challenges. With the emergence of new deep learning (DL) algorithms in recent years, methods to LUM with DL have achieved huge breakthroughs, which offers novel opportunities for the development of LUM for HSR-RSIs. This paper aims to provide a thorough review of recent achievements in this field. Existing high spatial resolution datasets in the research of semantic segmentation and single object segmentation are presented firstly. Next, we introduce several basic DL approaches that are frequently adopted for LUM. After reviewing DLbased LUM methods comprehensively, which highlights the contributions of researchers in the field of LUM for HSR-RSIs, we summarize these DL-based approaches based on two LUM criteria. Individually, the first one has supervised learning, semisupervised learning, or unsupervised learning, while another one is pixel-based or object-based. We then briefly review the fundamentals and the developments of the development of semantic segmentation and single object segmentation. At last, quantitative results that experiment on the dataset of ISPRS Vaihingen and ISPRS Potsdam are given for several representative models such as FCN and U-Net, following up with a comparison and discussion of the results.

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“…Semi-supervised learning [149] Utilize labeled and unlabeled input images to train ResNet, and a discriminator is also used as an auxiliary network for training.…”

Section: Semantic Segmentation Related To Dl-based Lummentioning

confidence: 99%

Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review

Zang

Cao

Wang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…(7) Large-scale datasets in remote sensing images such as UCMD [19], EuroSAT [20] and DOTA [21], have contributed to the development of the general object detection of remote sensing images. However, existing publicly available datasets in remote sensing only cover limited categories of objects [22][23][24][25]. There is no annotated dataset of fixed industrial facilities including thermal power plants, garbage dumps and sewage treatment plants to the best of our knowledge.…”

Section: Part_cls = Ce(p T ) = −α T Log(p T )mentioning

confidence: 99%

PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

et al. 2021

Self Cite

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The detection of Thermal Power Plants (TPPs) is a meaningful task for remote sensing image interpretation. It is a challenging task, because as facility objects TPPs are composed of various distinctive and irregular components. In this paper, we propose a novel end-to-end detection framework for TPPs based on deep convolutional neural networks. Specifically, based on the RetinaNet one-stage detector, a context attention multi-scale feature extraction network is proposed to fuse global spatial attention to strengthen the ability in representing irregular objects. In addition, we design a part-based attention module to adapt to TPPs containing distinctive components. Experiments show that the proposed method outperforms the state-of-the-art methods and can achieve 68.15% mean average precision.

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“…Object detection is an important task in the remote sensing image interpretation. With the application of deep learning in computer vision [1][2][3][4], an increasing number of object detection methods based on convolutional neural networks (CNNs) [5][6][7][8][9][10][11] have been proposed to achieve good performance. However, fully supervised object detection methods require a large number of samples with instance-level labels.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Pseudo-Label Generation for Weakly Supervised Object Detection in Remote Sensing Images

Wang

Qian

et al. 2021

Remote Sensing

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In recent years, fully supervised object detection methods in remote sensing images with good performance have been developed. However, this approach requires a large number of instance-level annotated samples that are relatively expensive to acquire. Therefore, weakly supervised learning using only image-level annotations has attracted much attention. Most of the weakly supervised object detection methods are based on multi-instance learning methods, and their performance depends on the process of scoring the candidate region proposals during training. In this process, the use of only image-level labels for supervision usually cannot obtain optimal results due to the lack of location information of the object. To address the above problem, a dynamic sample pseudo-label generation framework is proposed to generate pseudo-labels for each proposal without additional annotations. First, we propose the pseudo-label generation algorithm (PLG) to generate the category labels of the proposal by using the localization information of the object. Specifically, we propose to use the pixel average of the object’s localization map in the proposal as the proposal category confidence and calculate the pseudo-label by comparing the proposal category confidence with the preset threshold. In addition, an effective adaptive threshold selection strategy is designed to eliminate the effect of different category shape differences in computing sample pseudo-labels. Comparative experiments on the NWPU VHR-10 dataset demonstrate that our method can significantly improve the detection performance compared to existing methods.

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BAS$^{4}$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

Cited by 94 publications

References 46 publications

Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review

Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review

PCAN—Part-Based Context Attention Network for Thermal Power Plant Detection in Remote Sensing Imagery

Dynamic Pseudo-Label Generation for Weakly Supervised Object Detection in Remote Sensing Images

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