通过自适应信息进行高光谱目标检测-具有局部约束的理论度量学习

probability for fixed false alarm probability, such as orthogonal subspace projection (OSP) and adaptive

cosine/coherence estimator (ACE). The former OSP method proposed by Harsanyi et al. [

] suppresses

the background signatures by projecting each pixel’s spectrum onto a subspace, which is orthogonal

to the background signatures. The well-known ACE method proposed by Kraut et al. [

that the additive noise has been included in background, which is an unstructured background

into a potentially high-dimensional kernel space to solve the linearly inseparable problem in the

original space. Apparently, as mentioned in the article [

], kernel-based methods are also based on

statistical hypothesis test, and inherit the shortcomings of traditional target detection methods. It can

be concluded that kernel-based methods attempt to find a stable and credible feature space (distance

correlated. For decreasing spectral redundancy and releasing computational complexity, it is necessary

to reduce dimension by discarding redundant features for HSI target detection [

,

few target pixels of interest that HSI target detection rarely takes into consideration dimensionality

reduction, which may hide the accuracy of detecting targets. That is to say, target detection is usually

manifold smoothness regularization. Dong et al. [

] presented the maximum margin metric learning

(MMML) method, which utilizes the maximum margin framework as the objective function to learn

distance metric space and can maximally separate target samples from background ones without

certain assumptions. Dong et al. [

] presented random forest metric learning (RFML) method, which

adopts random forests as the underlying representation of the metric learning, to deal with limited

numbers of target samples by merging the standard relative position and the absolute pairwise position.

In general, by using metric learning, we can find the distance metric matrix, so as to transform the

original space into the metric feature space. Then, we can detect the desired targets, especially when

where the margin is defined as the difference between the between-class and within-class distances.

Furthermore, the information-theoretic metric learning (ITML) method, proposed by Davis et al. [

The major problem is that most methods mentioned above are global metric learning with global

method, which works in a weakly supervised manner, is innovatively introduced for hyperspectral

target detection with adaptively local constraints (ITML-ALC, for short). The proposed ITML-ALC

method explores adaptively local constraints to relax the fixed threshold, which can be used to compute

results of the proposed method using several challenging HSIs are detailed in Section 3, followed by

two key techniques of ITML. One is the ability to handle a wide variety of constraints and to optionally

denotes the relationship between the training samples

and

. Considering relationships of the

similarity or dissimilarity between pairs of samples, distances between samples in the same class can

be constrained as similar, and ones in different classes can be constrained as dissimilar. Then, we have

(or

,

and for any pairs

,