KEYWORDS: Hyperspectral imaging, Data modeling, Principal component analysis, Sun, Single crystal X-ray diffraction, Code division multiplexing, Signal to noise ratio, Speckle, Autoregressive models, Feature extraction
We propose an unsupervised method for slight change extraction and detection in multitemporal hyperspectral image sequence. To exploit the spectral signatures in hyperspectral images, autoregressive integrated moving average and fitting models are employed to create a prediction of single-band and multiband time series. Minimum mean absolute error index is then applied to obtain the preliminary change information image (PCII), which contains slight change information. After that, feature vectors are created for each pixel in the PCII using block processing and locally linear embedding. The final change detection (CD) mask is obtained by clustering the extracted feature vectors into changed and unchanged classes using k-means clustering algorithm with k=2. Experimental results demonstrate that the proposed method extracts the slight change information efficiently in the hyperspectral image sequence and outperforms the state-of-the-art CD methods quantitatively and qualitatively.
The recently-emerged compressive sensing (CS) theory goes against the Nyquist-Shannon (NS) sampling theory and
shows that signals can be recovered from far fewer samples than what the NS sampling theorem states. In this paper, to
solve the problems in image fusion step of the full-scene image mosaic for the multiple images acquired by a low-altitude
unmanned airship, a novel information mutual complement (IMC) model based on CS theory is proposed. IMC
model rests on a similar concept that was termed as the joint sparsity models (JSMs) in distributed compressive sensing
(DCS) theory, but the measurement matrix in our IMC model is rearranged in order for the multiple images to be
reconstructed as one combination. The experimental results of the BP and TSW-CS algorithm with our IMC model
certified the effectiveness and adaptability of this proposed approach, and demonstrated that it is possible to substantially
reduce the measurement rates of the signal ensemble with good performance in the compressive domain.