Hyperspectral Remote Sensing Data Analysis and Future Challenges

By José M. Bioucas-Dias, Antonio J. Plaza, Gustau Camps-Valls, Paul Scheunders, Nasser M. Nasrabadi, Jocelyn Chanussot. IEEE Geoscience and Remote Sensing Magazine, 2013

2,000 citations recorded by the source corpus.

Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.

Subfields: Analytical Chemistry, Media Technology

View Paper: https://www.semanticscholar.org/paper/0b29b743203ac9adb578bdbe4be6a8a68bdca132

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Citations in the corpus

Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015, neutral): “For this reason, hyperspectral data are used in a wide range of applications such as agriculture [3], mineralogy [4], surveillance [5], astronomy [7], and environmental sciences [9], [10].”
Deep Learning Advances in Computer Vision with 3D Data (2017, methodology): “Airborne or spaceborne sensors are often used for capturing HyperSpectral (HS) images typically in the form of a stack of N images of size [p1× p2] pixels representing the radiance in the respective band (also referred to as 3D hypercubes) [Bioucas-Dias et al. 2013]. Traditional methods for HS data ”
Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review (2022, background): “Tutorials RS (Bioucas-Dias et al., 2013) Introducing basic concepts and features of HS and its relevant topics (Rasti et al.”
Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, neutral): “It makes possible a precise analysis of soils and materials [4], [5].”
Deep Hyperspectral Image Sharpening (2018, neutral): “Rich spectral characteristics existed in hyperspectral images (HSIs) with hundreds of spectral bands have been demonstrated to improve the performance of many remote sensing [2]–[7] and computer vision [8], [9] applications.”
Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016, background): “In order to address the curse of dimensionality, feature extraction (FE) is considered as a crucial step in HSI processing [3].”
Deep learning classifiers for hyperspectral imaging: A review (2019, methodology): “Finally, advances in processing techniques such as batch partition and high performance computing (HPC) (Plaza et al., 2009; Lee et al., 2011; Bioucas-Dias et al., 2013), in particular on parallel and distributed architectures (Plaza and Chang, 2008; Plaza et al., 2011a), have allowed DNN models to ”
Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification (2022, background): “with the increasing spatial and spectral resolutions due to advances in spectral imaging technology [1].”
Hyperspectral Super-Resolution by Coupled Spectral Unmixing (2015, neutral): “This capability has proved useful for visual tasks such as tracking [23] and segmentation [22], in applications including face recognition [19], document analysis [18], food inspection [26] and earth observation [6].”
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, background): “To conclude this subsection devoted to the targets’ detection within hyperspectral data, there are other works that are worthy of being consulted [54] and some related references [71,97,98] that might complete and update some of the previously addressed approaches as, for example, anomaly detection ”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “As a result, it becomes difficult to identify the materials based on their spectral reflectance values [92].”
Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution (2019, background): “Therefore, HSIs have gained more and more usage in a lot of earth remote sensing tasks [1], [2], including object classiﬁcation [3]–[7] and change detection [8].”
Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network (2017, positive): “The rich spectral information of HSI is powerful, and has been widely employed in a range of successful applications in agriculture [1], environmental sciences [2], wild-land fire tracking, and biological threat detection [3].”
Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, background): “Among all the products that can be obtained from the acquired images, classification maps(1) are perhaps the most relevant ones [4], [5].”
Hyperspectral Image Classification With Deep Learning Models (2018, neutral): “[1]”
Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, background): “With respect to the spectral variability, which is brought by many factors such as incident illumination, atmospheric effects, unwanted shade and shadow, natural spectrum variation, and instrument noises [2], [4], [10],”