This paper evaluates the performance of 5 previously presented in the literature cluster validity indices for the Fuzzy C-Means (FCM) clustering algorithm. The first two indices, the Fuzzy Partition Coefficient (PC), Fuzzy Partition Entropy Coefficient (PEC) select the number of clusters for which the fuzzy partition is more “crisp-like” or less fuzzy. The other three indices are the Fuzzy Davies-Bouldin Index (FDB), Xie-Beni Index (XB), and the Index I (I) choose the number of clusters which maximizes the inter-cluster separation and minimizes the within cluster scatter. A modification to these three indices is proposed based on the Bhattacharyya distance between clusters. The results show that this modification improves upon the performance of Index I. On the data sets presented on this paper the modifications of indices FDB and XB performed adequately.
KEYWORDS: Nonuniformity corrections, Information technology, Temperature metrology, Staring arrays, Electronics, Infrared imaging, Black bodies, Control systems, Sensors, Target detection
In traditional designs of a NUC (non-uniformity correction) system, a rotating chopper-wheel (or a blurring/deform lens) is used to separate the outside scene and the inside FPN (fixed pattern noise) on the FPA (focal plane array). To design a NUC system removing the chopper-wheel (chopper-free) and its control electronics and hardware will not only considerably reduce the cost, but also require less space to fit the NUC system. In this paper, we describe a recently developed CF (chopper-free) NUC system. This system is simpler to build, costs less, and requires less space, as compared with traditional designs.
Recent developments of more sophisticated sensors enable the measurement of radiation in many more spectral intervals at a higher spectral resolution than previously possible. As the number of bands in high spectral resolution data increases, the capability to detect more objects and the detection accuracy should increase as well. Most of the detection techniques presently used in hyperspectral data require the use of spectral libraries that contain information on specific objects to be detected. An example of one technique used for detection purposes in hyperspectral imagery is the spectral angle approach based on the Euclidean inner product of the spectral signatures. This method has good performance on objects that have sufficient differences between their spectral signatures. This paper presents a partially supervised detection approach that uses previously measured spectral responses as inputs and is capable of differentiating objects that have similar spectral signatures. Two versions will be presented: one that is based on Statistical Pattern Recognition and other based on Fuzzy Pattern Recognition. The detection mechanisms are tested with objects of very similar spectral signatures and the detection results are compared with those from the spectral angle approach.
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