Multiscale Diff-Changed Feature Fusion Network for Hyperspectral Image Change Detection

Luo, Fulin; Zhou, Tianyuan; Liu, Jiamin; Guo, Tan; Gong, Xiuwen; Ren, Jinchang

doi:10.1109/tgrs.2023.3241097

Cited by 123 publications

(25 citation statements)

References 47 publications

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“…Gao et al [32] designed a depthwise crossattention module to extract not only self-correlation but also cross-correlation from diverse multisource data. Wang et al [33] proposed AM 3 Net which includes an involution operator, a spectral-spatial mutual-guided module, and a spectral-spatial mutual-guided module. Although these supervised learning-based methods have achieved excellent performance, their performance is highly dependent on the large-scale labeled training samples.…”

Section: A Supervised Multi-source Remote Sensing Data Classificationmentioning

confidence: 99%

“…W ITH the advancement of Earth observation technologies and satellite sensor platforms, significant amounts of remote sensing data have been obtained for a variety of purposes, such as vegetation mapping [1], coastal wetland classification [2], object detection [3], change detection [4], land cover classification [5], etc. Among these applications, land cover classification is fundamental to many operational mapping and reporting natural resource management programs.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these methods use deep neural networks for remote sensing data classification. Multiscale feature fusion [4], dual-branch network [12], hypergraph convolution [13] [14], Transformer-like network [15], attention multihop graph [16], and neural architecture search [17] have been used for hyperspectral or SAR data feature extraction. Although impressive results have been achieved, single modality classification can be problematic in some cases.…”

Section: Introductionmentioning

confidence: 99%

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SS-MAE: Spatial–Spectral Masked Autoencoder for Multisource Remote Sensing Image Classification

Lin,

Gao,

Shi

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Masked image modeling (MIM) is a highly popular and effective self-supervised learning method for image understanding. Existing MIM-based methods mostly focus on spatial feature modeling, neglecting spectral feature modeling. Meanwhile, existing MIM-based methods use Transformer for feature extraction, some local or high-frequency information may get lost. To this end, we propose a spatial-spectral masked auto-encoder (SS-MAE) for HSI and LiDAR/SAR data joint classification. Specifically, SS-MAE consists of a spatial-wise branch and a spectral-wise branch. The spatial-wise branch masks random patches and reconstructs missing pixels, while the spectral-wise branch masks random spectral channels and reconstructs missing channels. Our SS-MAE fully exploits the spatial and spectral representations of the input data. Furthermore, to complement local features in the training stage, we add two lightweight CNNs for feature extraction. Both global and local features are taken into account for feature modeling. To demonstrate the effectiveness of the proposed SS-MAE, we conduct extensive experiments on three publicly available datasets. Extensive experiments on three multi-source datasets verify the superiority of our SS-MAE compared with several state-of-the-art baselines. The source codes are available at https://github.com/summitgao/SS-MAE.

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Section: A Supervised Multi-source Remote Sensing Data Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SS-MAE: Spatial–Spectral Masked Autoencoder for Multisource Remote Sensing Image Classification

Lin,

Gao,

Shi

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…Similarly, aiming for the same objective, ASLFeat [28] introduces deformable convolutions to enhance the reliability and repeatability of keypoints. F. Luo et al [29] propose a multiscale diff-changed feature fusion network (MSDFFN) for change detection (CD) in hyperspectral images (HSI). This network enhances the feature representation by learning fine-grained change components between dual-temporal HSI at different scales.…”

Section: Current Challenges or Limits Of Prior Sotasmentioning

confidence: 99%

ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference

Chu,

Peng,

Luo

2023

PLoS ONE

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Simultaneous Localization and Mapping (SLAM) technology is crucial for achieving spatial localization and autonomous navigation. Finding image features that are representative presents a key challenge in visual SLAM systems. The widely used ORB (Oriented FAST and Rotating BRIEF) algorithm achieves rapid image feature extraction. However, traditional ORB algorithms face issues such as dense, overlapping feature points, and imbalanced distribution, resulting in mismatches and redundancies. This paper introduces an image feature extraction algorithm called Adaptive Threshold and Local Gray Difference-ORB(ALGD-ORB) to address these limitations. Specifically, an adaptive threshold is employed to enhance feature point detection, and an improved quadtree method is used to homogenize feature point distribution. This method combines feature descriptors generated from both gray size and gray difference to enhance feature descriptor distinctiveness. By fusing these descriptors, their effectiveness is improved. Experimental results demonstrate that the ALGD-ORB algorithm significantly enhances the uniformity of feature point distribution compared to other algorithms, while maintaining accuracy and real-time performance.

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“…According to deep learning-based change extraction techniques, current CD methods can be divided into two categories: supervised methods and unsupervised methods. Supervised methods train a CD model by large amounts of labeled remote sensing data [13]- [17]. Recently, supervised CD models based on vision transformer [18]- [21] have achieved outstanding performance.…”

Section: Introductionmentioning

confidence: 99%

UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation

Xu,

Shi,

Guo

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Change detection (CD) by comparing two bitemporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multi-temporal images. To this end, we propose a change detection with domain shift setting for remote sensing images. Furthermore, we present a novel unsupervised CD method using a light-weight transformer, called UCDFormer. Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency. After image translation, we can generate the difference map between the translated before-event image and the original after-event image. Then, a novel reliable pixel extraction module is proposed to select significantly changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy c-means clustering and adaptive threshold. Finally, a binary change map is obtained based on these selected pixel pairs and a binary classifier. Experimental results on different unsupervised CD tasks with seasonal and style changes demonstrate the effectiveness of the proposed UCDFormer. For example, compared with several other related methods, UCDFormer improves performance on the Kappa coefficient by more than 12%. In addition, UCDFormer achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications. The code is available at https://github.com/zhu-xlab/UCDFormer.

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Multiscale Diff-Changed Feature Fusion Network for Hyperspectral Image Change Detection

Abstract: Multi-scale diff-changed feature fusion network for hyperspectral image change detection. IEEE transactions on geoscience and remote sensing [online], Early Access.

Cited by 123 publications

References 47 publications

SS-MAE: Spatial–Spectral Masked Autoencoder for Multisource Remote Sensing Image Classification

SS-MAE: Spatial–Spectral Masked Autoencoder for Multisource Remote Sensing Image Classification

ALGD-ORB: An improved image feature extraction algorithm with adaptive threshold and local gray difference

UCDFormer: Unsupervised Change Detection Using a Transformer-Driven Image Translation

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