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Article
Geochemistry & Geophysics
Fulin Luo et al.
Summary: In this paper, a multiscale diff-changed feature fusion network (MSDFFN) is proposed for hyperspectral image change detection. The network improves the feature representation ability by learning the subtle changes between bitemporal HSIs at different scales. The method not only discovers the subtle changes in bitemporal HSIs, but also improves the discriminative power for HSI change detection.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Longfei Ren et al.
Summary: Hyperspectral unmixing is important for estimating pure spectral signatures in each pixel, but spectral variability can cause significant errors. To address this, a new method called orthogonal subspace unmixing (OSU) was developed, utilizing orthogonal subspace projection. By jointly performing subspace learning and unmixing, the proposed OSU method removes complex spectral variability. Experiments showed the effectiveness and superiority of this framework in mitigating spectral variability compared to classical linear unmixing methods or variability accounting approaches.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Jie Lei et al.
Summary: This article introduces a novel method for detecting hyperspectral image (HSI) targets with certain spatial information, referred to as the semantic transferred priori for hyperspectral target detection with spatial-spectral association (SSAD). By using transfer learning, a semantic segmentation network adapted for HSIs is designed to discriminate the spatial areas of targets, and a customized target spectrum is aggregated with those spectral pixels localized. Experiments demonstrate that our proposed method achieves higher detection accuracy and superior visual performance compared to the other benchmark methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Hongmin Gao et al.
Summary: Deep learning has become a popular method for hyperspectral target detection, but it faces challenges in handling training sample imbalance and achieving target highlighting and background suppression. Band selection is a new approach in hyperspectral target detection, and we propose a DL-based BS-HTD algorithm that combines deep learning-based band selection with deep learning-based target detection. The algorithm includes a multidepth and multibranch network (MDBN) for target detection and a band selection method that provides representative bands containing key target information. Through experiments on classical datasets, our DLBSTD algorithm demonstrates strong generalization and superior detection performance in target highlighting and background suppression compared to nine competitive algorithms.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Lianru Gao et al.
Summary: In this article, a new blind-spot self-supervised learning network (BS(3)LNet) is proposed for hyperspectral anomaly detection. The network is trained to reconstruct only background pixels instead of anomalous pixels by generating training patch pairs with blind spots. Experimental results show that the network is able to effectively separate anomalous pixels from the background in hyperspectral images and is competitive with other state-of-the-art approaches.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Lianru Gao et al.
Summary: Traditional hyperspectral anomaly detection methods rely heavily on specific model assumptions and cannot fully extract deep-level features to adapt to scene variability. This article proposes a novel chessboard topology-based anomaly detection (CTAD) method that directly mines high-dimensional data features, overcoming the limitations of traditional detectors and enabling adaptability and strong detection capabilities.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Yizhen Li et al.
Summary: In this paper, a novel deep graph network based on first-order smoothing is proposed for hyperspectral image (HSI) classification. A local and global topologically consistent graph is constructed to explore the union between fine pixel information and semantic superpixel information. A novel propagation procedure is proposed to address the oversmoothing problem. The results of experiments demonstrate the superiority of the proposed method over other state-of-the-art methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Cuiping Shi et al.
Summary: This article proposes a positive feedback spatial-spectral correlation network based on spectral interclass slicing (PFSSC_SICS) to address the issues of lack of labeled samples and spectral similarity among classes in hyperspectral image classification (HSIC). The PFSSC_SICS method incorporates a spectral interclass slicing strategy to remove similar spectral signatures between classes and improve classification performance. It also introduces a positive feedback mechanism and a spatial-spectral correlation module to extract deeper and more features. Experimental results demonstrate that PFSSC_SICS outperforms state-of-the-art methods in HSIC.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Munmun Baisantry et al.
Summary: In this paper, a supervised spectral-spatial band selection method is proposed, which combines the advantages of dimensionality reduction and feature extraction to improve classification accuracy.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Wenhui Guo et al.
Summary: This article proposes a novel deep framework for HSI classification problems, which integrates the spectral and spatial attention modules continuously to enhance the distinguishing ability of spectral bands and learn the spatial relevance between neighboring pixels. The feature-grouped strategy utilized in this framework can fully utilize the redundancy and difference of bands to obtain more valuable information.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Computer Science, Artificial Intelligence
Danfeng Hong et al.
Summary: This paper proposes a deep learning approach called endmember-guided unmixing network (EGU-Net) to improve the efficiency and accuracy of hyperspectral unmixing. By utilizing a two-stream Siamese deep network and adding spectrally meaningful constraints, EGU-Net is able to extract endmembers better and achieve more accurate and interpretable unmixing solutions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Geochemistry & Geophysics
Fei Ye et al.
Summary: Synthesizing a high-resolution hyperspectral image by merging a low-resolution hyperspectral image with a corresponding high-resolution multispectral image has shown promise as a super-resolution scheme. However, challenges such as spectral variability and the lack of predefined degradation operators remain. This paper introduces a novel fusion approach with a Bayesian framework, which addresses these issues by characterizing texture features and using Gaussian processes.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Zhonghao Chen et al.
Summary: In this article, a global to local hierarchical detection algorithm for hyperspectral image (HSI) is proposed, which effectively considers the spatial features of HSI for target detection. The proposed method addresses the problem of spectral variability and has been validated on four standard datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Jie Chen et al.
Summary: This article develops a BKG-annihilated TCIMF (BA-TCIMF) that can be implemented in two stages, with BKG annihilation in the first stage, and target detectability enhancement and target BKG suppression in the second stage.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Muhammad Ahmad et al.
Summary: This study introduces a fast and compact active learning method for hyperspectral image classification using 3-D CNN, which integrates deep transfer learning and active learning, achieving higher accuracy with very few training samples. By querying the most informative and heterogeneous samples from the validation set, the computational cost is reduced significantly.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Majid Seydgar et al.
Summary: This article proposes a deep probabilistic framework for generating reliable pseudo-labels from unlabeled samples to improve the classification performance of deep neural networks (DNNs). Experimental results demonstrate that the framework significantly enhances the generalization capacity of several state-of-the-art DNNs and achieves outstanding accuracy in HSI classification compared to competitors.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Zhu Han et al.
Summary: This article proposes an automatic neural architecture search model called AutoNAS for intelligent hyperspectral unmixing. The model determines the optimal network architecture by considering channel configurations and convolution kernels simultaneously, and achieves intelligent unmixing through self-supervised training and evolutionary algorithm.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Zhaoyue Wu et al.
Summary: This article proposes a novel relaxed CR detector for hyperspectral anomaly detection based on the collaborative representation model. The proposed method conducts CR on each feature dimension of the pixel under test and constrains the coding vectors of different features to be similar, effectively addressing the challenges faced by existing methods. Experimental results demonstrate that the proposed method performs well in suppressing background and outperforms other methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Bangyu Wu et al.
Summary: This article presents the application of a residual attention network (ResANet) to improve accuracy and efficiency in seismic impedance inversion.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Xiaobin Zhao et al.
Summary: This study proposes a new hyperspectral target detection method that utilizes a weighted Cauchy distance graph and a local adaptive collaborative representation strategy to fully utilize spatial and spectral information. Experimental results demonstrate that this method outperforms other algorithms in terms of detection performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Weiqiang Rao et al.
Summary: In this article, a Siamese transformer network for hyperspectral image target detection (STTD) is proposed to address the limitations of traditional statistical methods and machine learning methods in complex scenes. The STTD method constructs training samples, performs similarity metric learning, and applies a transformer network to extract global features for target detection. Experimental results demonstrate the superiority of the proposed STTD over state-of-the-art methods in suppressing background and highlighting targets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Geochemistry & Geophysics
Chein-I Chang et al.
Summary: This article develops three approaches to extend OSP in improving its performance, including data sphering, LRaSMD, and the combination of both methods. Experiments show that implementing OSP in conjunction with data sphering and LRaSMD significantly improves OSP in target detection and BKG suppression.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Peng Wang et al.
Summary: This study introduces a super-resolution mapping method based on spatial-spectral correlation to overcome the influences of imaging conditions on landcover class mapping. By combining spatial and spectral correlations, more accurate mapping results are obtained.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Renlong Hang et al.
Summary: An attention-aided CNN model for spectral-spatial classification of hyperspectral images is proposed, which integrates spectral and spatial attention subnetworks to focus on discriminative features. Experimental results demonstrate the superior performance of the proposed model compared to state-of-the-art CNN-related models.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Chein- Chang
Summary: ROC analysis is widely used to evaluate signal detection performance. This study introduces a 3D ROC curve for assessing detector effectiveness and target detectability and background suppressibility. By including a third independent variable, tau, new quantitative measures are designed to better evaluate detection performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Yanzi Shi et al.
Summary: This article introduces a new network block called RM for hyperspectral target detection and presents a DSSN method formed by stacking RM and DC blocks. The method can reduce spatial and spectral redundancies simultaneously, provide strong discrimination, and outperform other methods in detection performance and robustness, as demonstrated by experimental results.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Geochemistry & Geophysics
Dehui Zhu et al.
Summary: This article proposes a two-stream convolutional network-based target detector for hyperspectral images, extracting abundant spectral information and utilizing a hybrid sparse representation and classification strategy to select typical background pixels. A novel synthesis method is introduced to generate target samples, training the network with target and background samples to achieve superior performances in target detection.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2021)
Article
Engineering, Electrical & Electronic
Tongkai Cheng et al.
Summary: This article proposes a decomposition model with background dictionary learning for hyperspectral target detection, effectively addressing the recovery issues of background and target components using spatial smoothness and sparsity. The novel method demonstrates superiority in comparison with conventional and state-of-the-art target detectors through extensive experiments on simulated and real hyperspectral datasets.
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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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