Anomaly detection (AD) is an important application for target detection in remotely sensed hyperspectral data.
Therefore, variety kinds of methods with different advantages and drawbacks have been proposed for past two decades.
Recently, the kernelized support vector data description (SVDD) based anomaly detection approaches has become
popular as these methods avoid prior assumptions about the distribution of data and provides better generalization to
characterize the background. The global SVDD needs a training set for the background modeling; however, it is sensitive
to outliers in the data; so the training set has to be generated with pure background spectra. In general, the training data is
selected by random selection of the pixels spectra in entire image. In this study, we propose an approach for better
selection of the training data based on principal component analysis (PCA). A valid assumption for remotely sensed
images is that the principal components (PCs) with higher variance include substantial amount of background
information. For this reason, a subspace composed of several of the highest variance PCs of cluttered data can be defined
as background subspace. Thus, with the proposed algorithm, the selection of background pixels is achieved by projecting
all pixels in the image into the background subspace and thresholding them with respect to the relative energy on the
background subspace. Experimental results verify that the proposed algorithm has promising results in terms of accuracy
and speed during the detection of anomalies.
This paper proposes a novel automatic geo-spatial object recognition algorithm for high resolution satellite imaging. The proposed algorithm consists of two main steps; a hypothesis generation step with a local feature-based algorithm and a verification step with a shape-based approach. In the hypothesis generation step, a set of hypothesis for possible object locations is generated, aiming lower missed detections and higher false-positives by using a Bag of Visual Words type approach. In the verification step, the foreground objects are first extracted by a semi-supervised image segmentation algorithm, utilizing detection results from the previous step, and then, the shape descriptors for segmented objects are utilized to prune out the false positives. Based on simulation results, it can be argued that the proposed algorithm achieves both high precision and high recall rates as a result of taking advantage of both the local feature-based and the shape-based object detection approaches. The superiority of the proposed method is due to the ability of minimization of false alarm rate and since most of the object shapes contain more characteristic and discriminative information about their identity and functionality.