4 research outputs found
Exploring Hyperspectral Anomaly Detection with Human Vision: A Small Target Aware Detector
Hyperspectral anomaly detection (HAD) aims to localize pixel points whose
spectral features differ from the background. HAD is essential in scenarios of
unknown or camouflaged target features, such as water quality monitoring, crop
growth monitoring and camouflaged target detection, where prior information of
targets is difficult to obtain. Existing HAD methods aim to objectively detect
and distinguish background and anomalous spectra, which can be achieved almost
effortlessly by human perception. However, the underlying processes of human
visual perception are thought to be quite complex. In this paper, we analyze
hyperspectral image (HSI) features under human visual perception, and transfer
the solution process of HAD to the more robust feature space for the first
time. Specifically, we propose a small target aware detector (STAD), which
introduces saliency maps to capture HSI features closer to human visual
perception. STAD not only extracts more anomalous representations, but also
reduces the impact of low-confidence regions through a proposed small target
filter (STF). Furthermore, considering the possibility of HAD algorithms being
applied to edge devices, we propose a full connected network to convolutional
network knowledge distillation strategy. It can learn the spectral and spatial
features of the HSI while lightening the network. We train the network on the
HAD100 training set and validate the proposed method on the HAD100 test set.
Our method provides a new solution space for HAD that is closer to human visual
perception with high confidence. Sufficient experiments on real HSI with
multiple method comparisons demonstrate the excellent performance and unique
potential of the proposed method. The code is available at
https://github.com/majitao-xd/STAD-HAD
Superpixel guided deep-sparse-representation learning for hyperspectral image classification
This paper presents a new technique for hyperspectral image (HSI) classification by using superpixel guided deep-sparse-representation learning. The proposed technique constructs a hierarchical architecture by exploiting the sparse coding to learn the HSI representation. Specifically, a multiple-layer architecture using different superpixel maps is designed, where each superpixel map is generated by downsampling the superpixels gradually along with enlarged spatial regions for labeled samples. In each layer, sparse representation of pixels within every spatial region is computed to construct a histogram via the sum-pooling with normalization. Finally, the representations (features) learned from the multiple-layer network are aggregated and trained by a support vector machine classifier. The proposed technique has been evaluated over three public HSI data sets, including the Indian Pines image set, the Salinas image set, and the University of Pavia image set. Experiments show superior performance compared with the state-of-the-art methods