Optimal Band Selection for Hyperspectral Image Classification Based on Inter-Class Separability

Yin, Jingwei; Yi-song, Wang; Zhao, Zhijin

doi:10.1109/sopo.2010.5504325

Cited by 27 publications

(10 citation statements)

References 8 publications

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“…Representative clustering-based band selection methods include [41]- [44]. 4) Others: Some hybrid approaches, e.g., combining ranking and clustering [46]- [48], are proposed for band selection tasks. Furthermore, sparse learning, low rank representation, and deep learning also provide new insights [45], [49], [50].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification

Mou

Saha

Hua

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model training, improving accuracy, or both. It reduces redundancy among spectral bands while trying to preserve the original information of the image. By now, many efforts have been made to develop unsupervised band selection approaches, of which the majorities are heuristic algorithms devised by trial and error. In this article, we are interested in training an intelligent agent that, given a hyperspectral image, is capable of automatically learning policy to select an optimal band subset without any hand-engineered reasoning. To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning. Once the agent is trained, it learns a band-selection policy that guides the agent to sequentially select bands by fully exploiting the hyperspectral image and previously picked bands. Furthermore, we propose two different reward schemes for the environment simulation of deep reinforcement learning and compare them in experiments. This, to the best of our knowledge, is the first study that explores a deep reinforcement learning model for hyperspectral image analysis, thus opening a new door for future research and showcasing the great potential of deep reinforcement learning in Manuscript

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Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification

Mou

Saha

Hua

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Band selection methods select a subset of spectral features from the original data [9,10]. These methods can be split into six groups [11]: ranking-based methods [12][13][14], search-based methods [15,16], clustering-based methods [17][18][19], sparsity-based methods [20][21][22], embedding-learning-based methods [11], and hybrid-scheme-based methods [23][24][25]. Although band selection retains valuable bands for subsequent processing, the algorithms have a large computational burden and often are not robust in complex scenes.…”

Section: Introductionmentioning

confidence: 99%

Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

et al. 2021

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Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency.

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“…A supervised mutual-information-based band selection approach is proposed in Guo, Gunn, Damper, & Nelson (2006). Also, an approach that uses a spectral separability index is reported in Yin, Wang, & Zhao (2010). In Yang et al (2011), a supervised band selection method, which uses known spectra without examining original spectral bands of the considered data, is considered.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised hyperspectral band selection by combination of unmixing and sequential clustering techniques

Benabadji

Karoui

Djerriri

et al. 2018

European Journal of Remote Sensing

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Selecting the decisive spectral bands is a key issue in unsupervised hyperspectral band selection techniques. These methods are the most popular ways for dimensionality reduction of original data. A compact data representation without compromising the physical information and optimizing the separation between different materials are the main objectives of such selection processes. In this work, a hyperspectral band selection approach is proposed based on linear spectral unmixing and sequential clustering techniques. The use of these two specific techniques constitutes the main novelty of this investigation. The proposed approach operates in different successive steps. It starts with extracting material spectra contained in the considered data using an unmixing method. Then, the variance of extracted spectra samples is calculated at each wavelength, which results in a variances vector. This one is segmented into a fixed number of clusters using a sequential clustering strategy. Finally, only one spectral band is selected for each segment. This band corresponds to the wavelength at which a maximum variance value is obtained. Experiments on three real hyperspectral data demonstrate the superiority of the proposed approach in comparison with four methods from the literature.

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Optimal Band Selection for Hyperspectral Image Classification Based on Inter-Class Separability

Cited by 27 publications

References 8 publications

Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification

Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification

Mixed Noise Estimation Model for Optimized Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

Unsupervised hyperspectral band selection by combination of unmixing and sequential clustering techniques

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