Spatial weighted kernel spectral angle constraint method for hyperspectral change detection

Liu, Song; Song, Liyao; Li, Haiwei; Chen, Junyu; Zhang, Geng; Hu, Bingliang; Wang, Shuang; Liu, Siyuan

doi:10.1117/1.jrs.16.016503

Cited by 4 publications

(4 citation statements)

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“…4, we can conclude that all parameters have an impact on the proposed method. When we explore the influence of the ration k, we fix the dual-window size as (5,7), and the tradeoff parameters are set to (0.1,1,1). Then, we fix the parameter with the best AUC value in the last round and then explore the optimal value of the next parameter.…”

Section: Parameter Analysismentioning

confidence: 99%

“…Compared with visible and multispectral imagery, hyperspectral images (HSIs) contain hundreds of continuous spectral bands that provide spectral-spatial information. On this basis, HSI interpretation techniques have been widely applied in many fields, such as classification, 2 – 5 change detection, 6 – 9 and object detection 10 – 12 As an important subbranch of hyperspectral target location, hyperspectral anomaly detection (HAD) aims to distinguish objects of interest by capturing subtle spectral differences of ground objects without any prior information or supervision 13 , 14 .…”

Section: Introductionmentioning

confidence: 99%

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Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Wang,

Shi,

Cheng

et al. 2023

J. Appl. Rem. Sens.

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Hyperspectral anomaly detection (HAD) is a technique to find observations without prior knowledge, which is of particular interest as a branch of remote sensing object detection. However, the application of HAD is limited by various challenges, such as high-dimensional data, high intraclass variability, redundant information, and limited samples. To overcome these restrictions, we report an unsupervised strategy to implement HAD by dimensionality reduction (DR) and prior-based collaborative representation with adaptive global salient weight. The proposed framework includes three main steps. First, we select the most discriminating bands as the input hyperspectral images for subsequent processing in a DR manner. Then, we apply piecewise-smooth prior and local salient prior to collaborative representation to produce the initial detection map. Finally, to generate the final detection map, a global adaptive salient map is applied to the initial anomaly map to further highlight anomalies. Most importantly, the experimental results show that the proposed method outperforms alternative detectors on several datasets over different scenes. In particular, on the Gulfport dataset, the area under the curve value obtained by the proposed method is 0.9932, which is higher than the second-best method, convolutional neural network detector, by 0.0071.

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Section: Parameter Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Wang,

Shi,

Cheng

et al. 2023

J. Appl. Rem. Sens.

Self Cite

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show abstract

“…HSIs contain rich spectral information. Meanwhile, the 2D convolution neural networks have significantly affected computer vision, with applications including biomedical image classification [37], remote sensing image classification [3,38], change detection [39,40], and image deblurring [2]. However, when convolving along the spectral dimension of HSI, hundreds of bands need plenty of parameters.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images

Shi

Wang

et al. 2023

Drones

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An airborne hyperspectral imaging system is typically equipped on an aircraft or unmanned aerial vehicle (UAV) to capture ground scenes from an overlooking perspective. Due to the rotation of the aircraft or UAV, the same region of land cover may be imaged from different viewing angles. While humans can accurately recognize the same objects from different viewing angles, classification methods based on spectral-spatial features for airborne hyperspectral images exhibit significant errors. The existing methods primarily involve incorporating image or feature rotation angles into the network to improve its accuracy in classifying rotated images. However, these methods introduce additional parameters that need to be manually determined, which may not be optimal for all applications. This paper presents a spectral-spatial attention rotation-invariant classification network for the airborne hyperspectral image to address this issue. The proposed method does not require the introduction of additional rotation angle parameters. There are three modules in the proposed framework: the band selection module, the local spatial feature enhancement module, and the lightweight feature enhancement module. The band selection module suppresses redundant spectral channels, while the local spatial feature enhancement module generates a multi-angle parallel feature encoding network to improve the discrimination of the center pixel. The multi-angle parallel feature encoding network also learns the position relationship between each pixel, thus maintaining rotation invariance. The lightweight feature enhancement module is the last layer of the framework, which enhances important features and suppresses insignificance features. At the same time, a dynamically weighted cross-entropy loss is utilized as the loss function. This loss function adjusts the model’s sensitivity for samples with different categories according to the output in the training epoch. The proposed method is evaluated on five airborne hyperspectral image datasets covering urban and agricultural regions. Compared with other state-of-the-art classification algorithms, the method achieves the best classification accuracy and is capable of effectively extracting rotation-invariant features for urban and rural areas.

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“…As opposed to visible and multispectral images, hyperspectral images (HSIs) have been regarded as a remarkable invention in the field of remote sensing imaging sciences, due to their practical capacity to capture high-dimensional spectral information from different scenes on the Earth's surface [1]. HSIs consist of innumerable contiguous spectral bands that span the electromagnetic spectrum, providing rich and detailed physical attributes of land covers, which facilitate the development of various applications such as change detection [2][3][4][5], land-cover classification [6][7][8], retrieval [9], scene classification [10,11] and anomaly detection [12,13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection

Wang,

Shi,

et al. 2023

Remote Sensing

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Hyperspectral anomaly detection (HAD) is an important technique used to identify objects with spectral irregularity that can contribute to object-based image analysis. Latterly, significant attention has been given to HAD methods based on Autoencoders (AE). Nevertheless, due to a lack of prior information, transferring of modeling capacity, and the “curse of dimensionality”, AE-based detectors still have limited performance. To address the drawbacks, we propose a Multi-Prior Graph Autoencoder (MPGAE) with ranking-based band selection for HAD. There are three main components: the ranking-based band selection component, the adaptive salient weight component, and the graph autoencoder. First, the ranking-based band selection component removes redundant spectral channels by ranking the bands by employing piecewise-smooth first. Then, the adaptive salient weight component adjusts the reconstruction ability of the AE based on the salient prior, by calculating spectral-spatial features of the local context and the multivariate normal distribution of backgrounds. Finally, to preserve the geometric structure in the latent space, the graph autoencoder detects anomalies by obtaining reconstruction errors with a superpixel segmentation-based graph regularization. In particular, the loss function utilizes ℓ2,1-norm and adaptive salient weight to enhance the capacity of modeling anomaly patterns. Experimental results demonstrate that the proposed MPGAE effectively outperforms other state-of-the-art HAD detectors.

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Spatial weighted kernel spectral angle constraint method for hyperspectral change detection

Cited by 4 publications

References 0 publications

Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Spectral-Spatial Attention Rotation-Invariant Classification Network for Airborne Hyperspectral Images

Multi-Prior Graph Autoencoder with Ranking-Based Band Selection for Hyperspectral Anomaly Detection

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