Recent Advances in Multi- and Hyperspectral Image Analysis

Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span across various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from such highly dimensional data capturing up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been developing a range of image processing and machine learning analysis pipelines to process these kind of data as efficiently as possible. To this end, multi- or hyperspectral analysis has bloomed and has become an exciting research area which can enable the faster adoption of this technology in practice, also when such algorithms are deployed in hardware-constrained and extreme execution environments; e.g., on-board imaging satellites.

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Applications of hyperspectral image analysis for precision agriculture

Micro- and Nanotechnology Sensors, Systems, and Applications X ◽

10.1117/12.2303921 ◽

2018 ◽

Cited By ~ 3

Author(s):

Stan Martin ◽

Thomas George

Keyword(s):

Image Analysis ◽

Precision Agriculture ◽

Hyperspectral Image ◽

Hyperspectral Image Analysis

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Sample Generation with Self-Attention Generative Adversarial Adaptation Network (SaGAAN) for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs12050843 ◽

2020 ◽

Vol 12 (5) ◽

pp. 843 ◽

Cited By ~ 3

Author(s):

Wenzhi Zhao ◽

Xi Chen ◽

Jiage Chen ◽

Yang Qu

Keyword(s):

Hyperspectral Image ◽

Domain Adaptation ◽

Hyperspectral Data ◽

Generative Adversarial Networks ◽

High Quality ◽

Hyperspectral Image Analysis ◽

Spectral Bands ◽

Training Samples ◽

The Impact

Hyperspectral image analysis plays an important role in agriculture, mineral industry, and for military purposes. However, it is quite challenging when classifying high-dimensional hyperspectral data with few labeled samples. Currently, generative adversarial networks (GANs) have been widely used for sample generation, but it is difficult to acquire high-quality samples with unwanted noises and uncontrolled divergences. To generate high-quality hyperspectral samples, a self-attention generative adversarial adaptation network (SaGAAN) is proposed in this work. It aims to increase the number and quality of training samples to avoid the impact of over-fitting. Compared to the traditional GANs, the proposed method has two contributions: (1) it includes a domain adaptation term to constrain generated samples to be more realistic to the original ones; and (2) it uses the self-attention mechanism to capture the long-range dependencies across the spectral bands and further improve the quality of generated samples. To demonstrate the effectiveness of the proposed SaGAAN, we tested it on two well-known hyperspectral datasets: Pavia University and Indian Pines. The experiment results illustrate that the proposed method can greatly improve the classification accuracy, even with a small number of initial labeled samples.

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