Ensemble-Based Information Retrieval With Mass Estimation for Hyperspectral Target Detection

Sun, Xiaotong; Qu, Ying; Gao, Lianru; Sun, Xingming; Qi, Hairong; Zhang, Bing; Shen, Ting

doi:10.1109/tgrs.2021.3075583

Cited by 35 publications

(10 citation statements)

References 91 publications

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“…Given the core idea of applying topology to point set analysis is to achieve the ultimate evaluation purpose through the geometric deformation of space according to requirements of various tasks, it is crucial to chose the form of mapping to construct the corresponding topological space for AD. Binary tree is a hierarchical structure defined by branch relationship in machine learning [56], [57], exhibiting immense potential in data mining [58]. It could exploit the numerical differences between the anomaly and the background in different dimensions to achieve the separation between these two [59], which is highly compatible with high-dimensional data sets such as HSI.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based on Tree Topology for Hyperspectral Images

Sun

Zhang

Zhuang

et al. 2023

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

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As one of the most important research and application directions in hyperspectral remote sensing, anomaly detection (AD) aims to locate objects of interest within a specific scene by exploiting spectral feature differences between different types of land cover without any prior information. Most traditional AD algorithms are model-driven and describe hyperspectral data with specific assumptions, which cannot combat the distributional complexity of land covers in real scenes, resulting in a decrease in detection performance. To overcome the limitations of traditional algorithms, a novel tree topology-based anomaly detection (TTAD) method for hyperspectral images (HSIs) is proposed in this paper. TTAD departs from the single analytical mode based on specific assumptions but directly parses the HSI data itself. It makes full use of the "few and different" characteristics of anomalous data points that are sparsely distributed and far away from high-density populations. On this basis, topology, a powerful tool in mathematics that successfully handle multiple types of data mining tasks, is applied to anomaly detection to ensure sufficient feature extraction of land covers. First, the redistribution of HSI data is realized by constructing a tree-type topological space to improve the separability between anomalies and backgrounds. Then, topologically related subsets in this space are utilized to evaluate the abnormality degree of each sample in a data set, and detection results for the HSI are output accordingly. Abandoning traditional modeling but focusing on mining the data characteristics of HSI itself enables TTAD to better adapt to different complex scenes and locate anomalies with high precision. Experimental results on a large number of benchmark data sets demonstrate that TTAD could achieve excellent detection results with considerable computational efficiency. The proposed method exhibits superior comprehensive performance and is promising to be popularized in practical applications.

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Section: Introductionmentioning

confidence: 99%

Anomaly Detection Based on Tree Topology for Hyperspectral Images

Sun

Zhang

Zhuang

et al. 2023

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

Self Cite

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“…Hyperspectral image (HSI) is collected by the imaging spectrometer with tens or hundreds of narrow and contiguous spectral bands. Due to its abundant spectral characteristics and spatial information, HSI opens up possibilities for a broad range of fields, such as classification [1]- [5], target detection [6]- [10], spectral unmixing [11]- [15], and image fusion [16]- [18], in which classification is a fundamental task to identify the land-cover category of each hyperspectral pixel in HSI.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Spectral Involution MLP Network for Hyperspectral Image Classification

Shao

Liu

Yang

et al. 2022

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

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Recently, more and more multi-layer perceptron (MLP) -like models have been proposed. Among them, CycleMLP is good at dense feature prediction tasks, which is potentially useful for hyperspectral image (HSI) classification. However, the receptive field of CycleMLP tends to be cross-shaped, which will lead to insufficient spatial information extraction. Additionally, most of the HSI classification methods only use information from single HSI data. Lack of diversity in the features of a single modality limits classification performance. To address these issues, a novel spatial-spectral involution MLP network (SSIN) is proposed for HSI classification. SSIN contains two paths for extracting different kinds of information, namely the image path and the coordinate path. In the image path, we combine the MLP structure with the involution operation and propose involution MLP (InvoMLP). It obtains the spatial kernel weights corresponding to each pixel individually, thus improving the spatial interaction capability. At the same time, InvoMLP has the same receptive field range as conventional convolution, i.e., a rectangular receptive field. In the coordinate path, we build a lightweight module for extracting information. Unlike the information of images, the coordinates are intuitive information about the location distribution. Considering that the coordinate information contains the global spatial distribution of HSI, fusing it with the image information could improve long-distance dependencies of feature maps. Experimental results on four HSI datasets illustrate that SSIN can outperform some state-of-the-art methods.

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“…With the continuous expansion of remote sensing image applications, the demand for hyperspectral imaging (HSI) applications is also increasing, especially in the domains of land cover classification, specific target detection and recognition, environmental monitoring, and precision agriculture [1][2][3][4]. HSI data have many bands, and are highly susceptible to interference during the imaging process by a series of degradation phenomena, such as thermal noise, impulse noise, stripe noise, and dead lines.…”

Section: Introductionmentioning

confidence: 99%

Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks

Wang

Jiang

et al. 2022

Remote Sensing

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In hyperspectral imaging (HSI), stripe noise is one of the most common noise types that adversely affects its application. Convolutional neural networks (CNNs) have contributed to state-of-the-art performance in HSI destriping given their powerful feature extraction and learning capabilities. However, it is difficult to obtain paired training samples for real data. Most CNN destriping methods construct a paired training dataset with simulated stripe noise for network training. However, when the stripe noise of real data is complex, destriping performance of the model is constrained. To solve this problem, this study proposes a real HSI stripe removal method using a toward real HSI stripe removal via direction constraint hierarchical feature cascade network (TRS-DCHC). TRS-DCHC uses the stripe noise extract subnetwork to extract stripe patterns from real stripe-containing HSI data and incorporates clean images to form paired training samples. The destriping subnetwork advantageously utilizes a wavelet transform to explicitly decompose stripe and stripe-free components. It also adopts multi-scale feature dense connections and feature fusion to enrich feature information and deeply mine the discriminate features of stripe and stripe-free components. Our experiments on both simulated and real data of various loads showed that TRS-DCHC features better performance in both simulated and real data compared with state-of-the-art method.

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Ensemble-Based Information Retrieval With Mass Estimation for Hyperspectral Target Detection

Cited by 35 publications

References 91 publications

Anomaly Detection Based on Tree Topology for Hyperspectral Images

Anomaly Detection Based on Tree Topology for Hyperspectral Images

Spatial–Spectral Involution MLP Network for Hyperspectral Image Classification

Toward Real Hyperspectral Image Stripe Removal via Direction Constraint Hierarchical Feature Cascade Networks

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