Abstract
Because there are many unlabeled samples in hyperspectral images and the cost of manual labeling is high, this paper adopts semi-supervised learning method to make full use of many unlabeled samples. In addition, those hyperspectral images contain much spectral information and the convolutional neural networks have great ability in representation learning. This paper proposes a novel semi-supervised hyperspectral image classification framework which utilizes self-training to gradually assign highly confident pseudo labels to unlabeled samples by clustering and employs spatial constraints to regulate self-training process. Spatial constraints are introduced to exploit the spatial consistency within the image to correct and re-assign the mistakenly classified pseudo labels. Through the process of self-training, the sample points of high confidence are gradually increase, and they are added to the corresponding semantic classes, which makes semantic constraints gradually enhanced. At the same time, the increase in high confidence pseudo labels also contributes to regional consistency within hyperspectral images, which highlights the role of spatial constraints and improves the HSIc efficiency. Extensive experiments in HSIc demonstrate the effectiveness, robustness, and high accuracy of our approach.
Highlights
Due to the advance of optical sensing technology, hyperspectral images, which contain richer spectral information compared with Synthetic-Aperture Radar (SAR) and Red_Green_Blue (RGB) images, have attracted increasing attentions in the remote sensing field recently
A simple description of Figure 1: (1) Preprocessing of the image, the hyperspectral image is divided into slice spectral-slices according to the number of spectral channels; (2) convolutional neural networks (CNN) performs spectral-spatial feature extraction for each spectral-slice separately; (3) The feature set extracted from each spectral-slice is separately constrained clustering to obtain the clustering results of all spectral-slices; (4) Similar to finding intersections between sets, the clustering results of each spectral-slice are compared
We introduce a novel semi-supervised classification algorithm for hyperspectral image classification (HSIc) based on the cooperation between deep learning models and clustering
Summary
Due to the advance of optical sensing technology, hyperspectral images, which contain richer spectral information compared with Synthetic-Aperture Radar (SAR) and Red_Green_Blue (RGB) images, have attracted increasing attentions in the remote sensing field recently. Compared to the application of SAR or RGB images [6,7,8], there are two main challenges for HSIc: (1) redundancy of spectral information and (2) large datasets. Since the redundancy of spectral information about hyperspectral images, dimensionality reduction [9,10] is required to efficiently extract features. Chen et al [11] applied Principal Component Analysis (PCA) [12,13] for dimension reduction to reduce redundant spectral features by linearly transposing raw high-dimensional data into a new low-dimensional data. In the process of dimensionality reduction, the original spectral structure may be destroyed, resulting in the loss of some useful spectral information, possibly reducing the performance of the HSIc
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