Deep Feature Reconstruction Learning for Open-Set Classification of Remote-Sensing Imagery

Sun, Hao; Li, Qianqian; Yu, Jie; Zhou, Dongbo; Chen, Wenjing; Zheng, Xiangtao; Lu, Xiaoqiang

doi:10.1109/lgrs.2023.3295871

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…Detailed structures of the gated multi-scale information transmitting module are shown in Figure 2 . Three dilated convolutions with different dilation rates (e.g., [ 1 , 2 , 4 ]) are firstly used in parallel to extract the features of different scales [ 54 ]. Then, these features are concatenated and fed to

convolutions to generate multi-scale features

.…”

Section: Methodsmentioning

confidence: 99%

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Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Wang,

Cao,

Huang

2024

Entropy

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Low-light image enhancement (LLIE) aims to improve the visual quality of images taken under complex low-light conditions. Recent works focus on carefully designing Retinex-based methods or end-to-end networks based on deep learning for LLIE. However, these works usually utilize pixel-level error functions to optimize models and have difficulty effectively modeling the real visual errors between the enhanced images and the normally exposed images. In this paper, we propose an adaptive dual aggregation network with normalizing flows (ADANF) for LLIE. First, an adaptive dual aggregation encoder is built to fully explore the global properties and local details of the low-light images for extracting illumination-robust features. Next, a reversible normalizing flow decoder is utilized to model real visual errors between enhanced and normally exposed images by mapping images into underlying data distributions. Finally, to further improve the quality of the enhanced images, a gated multi-scale information transmitting module is leveraged to introduce the multi-scale information from the adaptive dual aggregation encoder into the normalizing flow decoder. Extensive experiments on paired and unpaired datasets have verified the effectiveness of the proposed ADANF.

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convolutions to generate multi-scale features

.…”

Section: Methodsmentioning

confidence: 99%

“…[ 3 ]. Low-light image enhancement can effectively improve the performance of methods such as object detection and scene understanding at night or in low-light conditions [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Wang,

Cao,

Huang

2024

Entropy

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“…Unlike natural images and traditional grayscale images, hyperspectral images (HSIs) collected through hyperspectral sensors contain over a hundred spectral bands for the same scene [1][2][3]. Benefiting from the richness of spectral information, HSIs play a crucial role in earth observation, such as target detection [4], mineral exploration [5], image classification [6], and more. However, due to the complexity and uncertainty of imaging, HSIs inevitably suffer from noise interference, including Gaussian noise, striping noise, and mixed noise [7].…”

Section: Introductionmentioning

confidence: 99%

Memory Augmentation and Non-Local Spectral Attention for Hyperspectral Denoising

Dong,

Mo,

Sun

et al. 2024

Remote Sensing

Self Cite

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In this paper, a novel hyperspectral denoising method is proposed, aiming at restoring clean images from images disturbed by complex noise. Previous denoising methods have mostly focused on exploring the spatial and spectral correlations of hyperspectral data. The performances of these methods are often limited by the effective information of the neighboring bands of the image patches in the spectral dimension, as the neighboring bands often suffer from similar noise interference. On the contrary, this study designed a cross-band non-local attention module with the aim of finding the optimal similar band for the input band. To avoid being limited to neighboring bands, this study also set up a memory library that can remember the detailed information of each input band during denoising training, fully learning the spectral information of the data. In addition, we use dense connected module to extract multi-scale spatial information from images separately. The proposed network is validated on both synthetic and real data. Compared with other recent hyperspectral denoising methods, the proposed method not only demonstrates good performance but also achieves better generalization.

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“…Remote sensing is an imaging technique that utilises multiple wavelengths to obtain detailed information about surface materials and has been widely used in many fields [5–7], such as land‐use planning, natural resources and environmental monitoring. At the provincial and municipal scale, Wu et al.…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing is an imaging technique that utilises multiple wavelengths to obtain detailed information about surface materials and has been widely used in many fields [5][6][7], such as land-use planning, natural resources and environmental monitoring. At the provincial and municipal scale, Wu et al [8] used multi-source remote sensing data such as MODIS and VIIRS night light data to investigate the driving factors of the urban heat island in Guangdong Province.…”

Section: Introductionmentioning

confidence: 99%

Exploring the spatiotemporal relationship between green infrastructure and urban heat island under multi‐source remote sensing imagery: A case study of Fuzhou City

Hong,

Huang,

Chen

et al. 2023

CAAI Trans on Intel Tech

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Green Infrastructure (GI) has garnered increasing attention from various regions due to its potential to mitigate urban heat island (UHI), which has been exacerbated by global climate change. This study focuses on the central area of Fuzhou city, one of the “furnace” cities, and aims to explore the correlation between the GI pattern and land surface temperature (LST) in the spring and autumn seasons. The research adopts a multiscale approach, starting from the urban scale and using urban geographic spatial characteristics, multispectral remote sensing data, and morphological spatial pattern analysis (MSPA). Significant MSPA elements were tested and combined with LST to conduct a geographic weighted regression (GWR) experiment. The findings reveal that the UHI in the central area of Fuzhou city has a spatial characteristic of “high temperature in the middle and low temperature around,” which is coupled with a “central scattered and peripheral concentrated” distribution of GI. This suggests that remote sensing data can effectively be utilised for UHI inversion. Additionally, the study finds that the complexity of GI, whether from the perspective of the overall GI pattern or the classification study based on the proportion of the core area, has an impact on the alleviation of UHI in both seasons. In conclusion, this study underscores the importance of a reasonable layout of urban green infrastructure for mitigating UHI.

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Deep Feature Reconstruction Learning for Open-Set Classification of Remote-Sensing Imagery

Cited by 11 publications

References 16 publications

Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Adaptive Dual Aggregation Network with Normalizing Flows for Low-Light Image Enhancement

Memory Augmentation and Non-Local Spectral Attention for Hyperspectral Denoising

Exploring the spatiotemporal relationship between green infrastructure and urban heat island under multi‐source remote sensing imagery: A case study of Fuzhou City

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