Spatially adaptive semi‐supervised learning with Gaussian processes for hyperspectral data analysis

This paper presents a semi‐supervised learning algorithm called Gaussian process expectation‐maximization (GP‐EM), for classification of landcover based on hyperspectral data analysis. Model parameters for each land cover class are first estimated by a supervised algorithm using Gaussian process regressions to find spatially adaptive parameters, and the estimated parameters are then used to initialize a spatially adaptive mixture‐of‐Gaussians model. The mixture model is updated by expectation‐maximization iterations using the unlabeled data, and the spatially adaptive parameters for unlabeled instances are obtained by Gaussian process regressions with soft assignments. Spatially and temporally distant hyperspectral images taken from the Botswana area by the NASA EO‐1 satellite are used for experiments. Detailed empirical evaluations show that the proposed framework performs significantly better than all previously reported results by a wide variety of alternative approaches and algorithms on the same datasets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 358–371, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87150/1/10119_ftp.pd

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin