Abstract
In recent years, deep learning has dramatically improved the cognitive ability of the network by extracting depth features, and has been successfully applied in the field of feature extraction and classification of hyperspectral images. However, it is facing great difficulties for target detection due to extremely limited available labeled samples that are insufficient to train deep networks. In this paper, a novel target detection framework for deep learning is proposed, denoted as HTD-Net. To overcome the few-training-sample issue, the proposed framework utilizes an improved autoencoder (AE) to generate target signatures, and then finds background samples which differ significantly from target samples based on a linear prediction (LP) strategy. Then, the obtained target and background samples are used to enlarge the training set by generating pixel-pairs, which is viewed as the input of a pre-designed network architecture to learn discriminative similarity. During testing, pixel-pairs of a pixel to be labeled are constructed with both available target samples and background samples. Spectral difference between these pixel-pairs is classified by the well-trained network with results of similarity measurement. The outputs from a two-branch averaged similarity scores are combined to generate the final detection. Experimental results with several real hyperspectral data demonstrate the superiority of the proposed algorithm compared to some traditional target detectors.
Highlights
Hyperspectral remote sensing usually uses hundreds of narrow contiguous wavelength bands to obtain rich spectral information
We verify the detection performance of the proposed HTD-Net, and compare it with existing target detection algorithms, such as the target detector based on sparse representation (SR-TD), the target detector based on collaborative representation (CR-TD), and the traditional adaptive coherence estimator (ACE) [7]
A convolutional neural network (CNN)-based algorithm using only few target signatures has been proposed for hyperspectral target detection, denoted as HTD-Net
Summary
Hyperspectral remote sensing usually uses hundreds of narrow contiguous wavelength bands to obtain rich spectral information. Target detection in hyperspectral images is a crucial task in the field of hyperspectral remote sensing, which seeks to discriminate human-made targets that are different from the natural background from the perspective of spectral features. In [25], the above network was improved, and CNN can directly learn structural features from the input data, similar to different spectral band-pass filters. In the light of this issue, a hyperspectral target detection framework using a deep network with data augmentation (denoted as HTD -Net) is proposed, where an improved autoencoder (AE) [31] is firstly employed to generate target signatures and select distinctive background samples that are the most different from target samples in the detecting image with the criterion of linear prediction (LP) [32].
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