Joint Characterization of the Cryospheric Spectral Feature Space

• URL: http://arxiv.org/abs/2112.01416v1
• Date: Thu, 2 Dec 2021 17:04:50 GMT
• Title: Joint Characterization of the Cryospheric Spectral Feature Space
• Authors: Christopher Small, Daniel Sousa
• Abstract summary: characterization of feature space dimensionality, geometry and topology can provide guidance for effective model design. This study compares and contrast two approaches for identifying feature space basis vectors via dimensionality reduction. Joint characterization reveals distinct continua for snow-firn gradients on different parts of the Greenland Ice Sheet. The ability of PC+t-SNE joint characterization to produce a physically interpretable spectral feature spaces suggests that this characterization might be extended to the much higher dimensional hyperspectral feature space of all terrestrial land cover.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hyperspectral feature spaces are useful for many remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In such cases, characterization of the feature space dimensionality, geometry and topology can provide guidance for effective model design. The objective of this study is to compare and contrast two approaches for identifying feature space basis vectors via dimensionality reduction. These approaches can be combined to render a joint characterization that reveals spectral properties not apparent using either approach alone. We use a diverse collection of AVIRIS-NG reflectance spectra of the snow-firn-ice continuum to illustrate the utility of joint characterization and identify physical properties inferred from the spectra. Spectral feature spaces combining principal components (PCs) and t-distributed Stochastic Neighbor Embeddings (t-SNEs) provide physically interpretable dimensions representing the global (PC) structure of cryospheric reflectance properties and local (t-SNE) manifold structures revealing clustering not resolved in the global continuum. Joint characterization reveals distinct continua for snow-firn gradients on different parts of the Greenland Ice Sheet and multiple clusters of ice reflectance properties common to both glacier and sea ice in different locations. Clustering revealed in t-SNE feature spaces, and extended to the joint characterization, distinguishes differences in spectral curvature specific to location within the snow accumulation zone, and BRDF effects related to view geometry. The ability of PC+t-SNE joint characterization to produce a physically interpretable spectral feature spaces revealing global topology while preserving local manifold structures suggests that this characterization might be extended to the much higher dimensional hyperspectral feature space of all terrestrial land cover.

Related papers

• IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion [97.58125811599383]
  Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge. We propose a novel Multi-curvature shared and specific Embedding (IME) model for TKGC tasks. IME incorporates two key properties, namely space-shared property and space-specific property.
  arXiv  Detail & Related papers  (2024-03-28T23:31:25Z)
• Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues [6.580484964018551]
  Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. We propose to investigate the inter-dependencies in their respective hyperspectral space. These embedded features can be fully exploited by querying the inter-channel correlations.
  arXiv  Detail & Related papers  (2023-12-18T11:37:19Z)
• Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution [47.12985199570964]
  We propose a novel cross-scope spatial-spectral Transformer (CST) to investigate long-range spatial and spectral similarities for single hyperspectral image super-resolution. Specifically, we devise cross-attention mechanisms in spatial and spectral dimensions to comprehensively model the long-range spatial-spectral characteristics. Experiments over three hyperspectral datasets demonstrate that the proposed CST is superior to other state-of-the-art methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2023-11-29T03:38:56Z)
• Neural Spectro-polarimetric Fields [23.54056495576914]
  We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals. We introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes.
  arXiv  Detail & Related papers  (2023-06-21T21:00:46Z)
• Discovering Class-Specific GAN Controls for Semantic Image Synthesis [73.91655061467988]
  We propose a novel method for finding spatially disentangled class-specific directions in the latent space of pretrained SIS models. We show that the latent directions found by our method can effectively control the local appearance of semantic classes.
  arXiv  Detail & Related papers  (2022-12-02T21:39:26Z)
• Shape And Structure Preserving Differential Privacy [70.08490462870144]
  We show how the gradient of the squared distance function offers better control over sensitivity than the Laplace mechanism. We also show how using the gradient of the squared distance function offers better control over sensitivity than the Laplace mechanism.
  arXiv  Detail & Related papers  (2022-09-21T18:14:38Z)
• Spectral-Spatial Global Graph Reasoning for Hyperspectral Image Classification [50.899576891296235]
  Convolutional neural networks have been widely applied to hyperspectral image classification. Recent methods attempt to address this issue by performing graph convolutions on spatial topologies.
  arXiv  Detail & Related papers  (2021-06-26T06:24:51Z)
• Joint Characterization of Multiscale Information in High Dimensional Data [0.0]
  We propose a multiscale joint characterization approach designed to exploit synergies between global and local approaches to dimensionality reduction. We show that joint characterization is capable of detecting and isolating signals which are not evident from either PCA or t-sne alone.
  arXiv  Detail & Related papers  (2021-02-18T23:33:00Z)
• SU$(3)_1$ Chiral Spin Liquid on the Square Lattice: a View from Symmetric PEPS [55.41644538483948]
  Quantum spin liquids can be faithfully represented and efficiently characterized within the framework of Projectedangled Pair States (PEPS) Characteristic features are revealed by the entanglement spectrum (ES) on an infinitely long cylinder. Special features in the ES are shown to be in correspondence with bulk anyonic correlations, indicating a fine structure in the holographic bulk-edge correspondence.
  arXiv  Detail & Related papers  (2019-12-31T16:30:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.