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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC Irvine Electronic Theses and Dissertations (2018)

Vision systems use a pipeline of feature extraction and analysis to predict the desired output from input image data. With robust features, we can achieve high accuracy using care- fully chosen, but simple analysis. Learning features and using features effectively are two problems we focus on in this work. We propose a novel formulation of convolutional sparse coding called spherical sparse coding (SSC). SSC removes the need for iterative optimization to compute the sparse codes (features) for the typical least squares formulation. Using the SSC formulation, we show a clear connection between convolutional sparse coding and con- volutional neural networks (CNNs). We extend SSC to a supervised method for classification that uses codes that are biased by a hypothesized class. These class-specific codes can be re- constructed to give us images that maximize the classification score of the hypothesized class. Next, we propose using the Siamense network architecture with the multi-channel normalized cross-correlation (MCNCC) similarity metric for cross-domain image matching. We show that our choices in how we use features can have significant performance consequences; even prior to learning any parameters, we achieve state-of-the-art performance using off-the-shelf CNN features with MCNCC on a number of different cross-domain matching problems.

    Cover page: Learning Robust Features and Metrics for Image Classification and MatchingCreative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2019)
    In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.
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