Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images

Yang, Kunping; Tong, Xinyi; Xia, Gui-Song; Shen, Weiming; Zhang, Liangpei

doi:10.1109/tgrs.2022.3197334

Cited by 12 publications

(6 citation statements)

References 56 publications

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“…To further validate the generalisation of the SABNet network, we conducted a series of comparison experiments using the GID-15 dataset with 15 classes and seven other advanced land cover classification methods, namely: BiseNet [22], PSPNet [17], Segformer [41], UNet [16], Deeplabv3+ [26], AMFFNet [48], HPSNet [53] and CAFHNet [54], and calculated three quantitative metrics as well as the number of parameters and computation of the model for the different network experimental results.…”

Section: G Comparison With Other Advanced Network On the Gid-15 Datasetmentioning

confidence: 99%

SABNet: Self-Attention Bilateral Network for Land Cover Classification

Hu,

Qian,

Xiao

et al. 2024

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

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Land cover classification has been of great interest as one of the most prominent applications of remote sensing images. The emergence of convolutional neural networks has largely promoted the development of land cover classification, but it ignores the positional relationship between pixels. When remotely sensed features have both large intraclass scale differences and interclass similarities, it will result in the problems of fuzzy class boundaries of classification results and misclassification of small samples, which are difficult to be solved by existing methods. Inspired by the recent Transformer network, we propose a selfattentive bilateral network SABNet to alleviate these problems. Its backbone consists of a modified multiscale vision transformer and a stacked convolutional layer for extracting global spatial information and local contextual information. A local embedding module and a coordinate attention fusion module are further proposed in the feature fusion stage to reduce attention distraction and efficiently fuse the high and low features. A stepwise feature fusion module is proposed in the decoder to fully fuse the features extracted from the two branches. Experiments show that our method achieves the best results in mIoU on both Landcover.ai and GID-15 datasets with a similar number of parameters, 91.49% for the Landcover.ai dataset and 64.23% for the GID-15 dataset, compared with existing methods.

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Section: G Comparison With Other Advanced Network On the Gid-15 Datasetmentioning

confidence: 99%

SABNet: Self-Attention Bilateral Network for Land Cover Classification

Hu,

Qian,

Xiao

et al. 2024

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

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“…To reduce the gap between high-resolution land cover datasets and real-world application requirements, we reorganize and augment the category system of the land cover dataset GID. GID is available in versions with 5/15 classes; interested readers can refer to Tong et al (2020) and Yang et al (2022) . Our new dataset, named Five-Billion-Pixels , consists of

GF-2 satellite images annotated in a more complete category system (see Fig.…”

Section: Study Datamentioning

confidence: 99%

“…But most of them have geographical coverage areas below

km 2 and are located in concentrated regions, such as ISPRS Potsdam ( ISPRS-Contest, 2018 ), ISPRS Vaihingen ( ISPRS-Contest, 2018 ), Zurich Summer ( Volpi and Ferrari, 2015 ), RIT-18 ( Kemker et al, 2018 ), and Zeebruges ( Marcos et al, 2018 ). Existing large-scale datasets, with coverage areas more than

km 2 and wide geographical distributions, are typically annotated with about 10 classes, and do not contain detailed urban functional categories, including SpaceNet ( Van Etten et al, 2018 ), DeepGlobe ( Demir et al, 2018 ), MiniFrance ( Castillo-Navarro et al, 2021 ), Gaofen Image Dataset (GID) ( Tong et al, 2020 , Yang et al, 2022 ), and LandCoverNet ( Alemohammad and Booth, 2020 ). Although these large-scale datasets possess adequate data amount and data diversity, their incomplete land cover category systems prevent them from fully bridging the gap between algorithmic research and real-world applications.…”

Section: Introductionmentioning

confidence: 99%

“…To address the above-mentioned problems, in this paper, we propose the Five-Billion-Pixels dataset, which extends the land cover dataset GID ( Tong et al, 2020 , Yang et al, 2022 ). Instead of 5/15 basic categories of GID, it contains more than 5 billion labeled pixels of

high-resolution Gaofen-2 (GF-2) satellite images annotated in a more complete category system, consisting of 24 land use and land cover classes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enabling country-scale land cover mapping with meter-resolution satellite imagery

Tong¹,

Xia²,

Zhu³

2023

ISPRS Journal of Photogrammetry and Remote Sensing

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“…Sharma et al [25] analyzed the spatial relationship between a single pixel of a remote sensing image and its neighborhood, and proposed a depth learning framework based on a spatial neighborhood patch, which can effectively classify the remote sensing image with medium resolution. Starting from the mathematical analysis of parameter optimization, Yang et al [26] designed a network called HPS_Net, which can adjust the relationship between feature maps and pixel path selection, and can effectively segment ground objects. Due to the complex features of remote sensing images, a relatively large model is required to capture the features, so designing a small model and achieving good results is also a research direction.…”

Section: A Cnn For Remote Sensing Imagementioning

confidence: 99%

DSViT: Dynamically Scalable Vision Transformer for Remote Sensing Image Segmentation and Classification

Wang,

Ji,

Wang

2023

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

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The relationship between the foreground target and the background of remote sensing image is very complex. The vision task of remote sensing image faces the problems of complex targets and unbalanced categories. These problems make the modeling method have further improvement space. Therefore, this article proposes a dynamically scalable attention model that combines convolutional features and Transformer features. It can dynamically select the model depth according to the size of the input image, which alleviates the problem of insufficient global information extraction of the single convolution model and the computational overhead limitation of the pure Transformer model. We validated the model on two public remote sensing image classifications and two remote sensing image segmentation datasets. The accuracy and mean pixel accuracy (mPA) of the method in this article reached 96.16% and 93.44%, respectively, on the university of california (UC) Merced classification dataset. Compared with some recent work, the method has a net improvement of 5.0% and 4.82% over the pyramid vision transformer (PVT) model. On the Potsdam segmentation dataset, the accuracy and F1 of the transformer and CNN hybrid neural network (TCHNN) model are 91.5% and 92.86%, respectively. The performance of the method has improved 0.64% and 1.0%, and the other two datasets have also achieved the best results.

show abstract

Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images

Cited by 12 publications

References 56 publications

SABNet: Self-Attention Bilateral Network for Land Cover Classification

SABNet: Self-Attention Bilateral Network for Land Cover Classification

Enabling country-scale land cover mapping with meter-resolution satellite imagery

DSViT: Dynamically Scalable Vision Transformer for Remote Sensing Image Segmentation and Classification

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