Abstract
ABSTRACTIt is of great interest in exploiting spectral-spatial information for hyperspectral image (HSI) classification at different spatial resolutions. This paper proposes a new spectral-spatial deep learning-based classification paradigm. First, pixel-based scale transformation and class separability criteria are employed to measure appropriate spatial resolution HSI, and then we integrate the spectral and spatial information (i.e., both implicit and explicit features) together to construct a joint spectral-spatial feature set. Second, as a deep learning architecture, stacked sparse autoencoder provides strong learning performance and is expected to exploit even more abstract and high-level feature representations from both spectral and spatial domains. Specifically, random forest (RF) classifier is first introduced into stacked sparse autoencoder for HSI classification, based on the fact that it provides better tradeoff among generalization performance, prediction accuracy and operation speed compared to other traditional procedures. Experiments on two real HSIs demonstrate that the proposed framework generates competitive performance.
Highlights
Hyperspectral imagery has been widely available as a result of advances in remote sensors, and it has high spectral resolution and contains abundant spectral information [Heras et al, 2014; Li et al, 2015]
Random forest (RF) classifier is first introduced into stacked sparse autoencoder for hyperspectral image (HSI) classification, based on the fact that it provides better tradeoff among generalization performance, prediction accuracy and operation speed compared to other traditional procedures
The appropriate scale image with 40m spatial resolution is utilized in this scenario, and the specific selection criteria for the role will be explained
Summary
Hyperspectral imagery has been widely available as a result of advances in remote sensors, and it has high spectral resolution and contains abundant spectral information [Heras et al, 2014; Li et al, 2015] This characteristic makes it possible to detect and discriminate the subtle differences among land cover classes. The high dimensionality and complexity of spectral data sets have stimulated the development of several advanced methodologies, such as the wellknown support vector machine (SVM) classifier and sparse representation-based classification (SRC). These two kind of classifiers and their derivatives have been widely used for addressing the ill-posed classification problems of high dimensional data space [Melgani and Bruzzone, 2004; Guo et al, 2008; Qian et al, 2012]. To further improve the classification performance, extensive studies have been developed to incorporate spatial information of HSI, based on the assumption that pixels in a local region generally belonging to the same class [Li et al, 2012; Fang et al, 2014]
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