Due to the large field angle and large geometric distortions of the HaiYang-1C ultraviolet imager (UVI), a single rational function model (SRFM) is unable to fully absorb the geometric distortions. The SRFM-based sensor orientation accuracy of the UVI images is thereby worse than expected. In order to improve the sensor orientation accuracy, a feasible sensor orientation method for the UVI images based on a piecewise rational function model (PRFM) is proposed. A complete UVI image is first logistically divided into five subimages, and the adjacent subimages have an overlap. Then, an SRFM is used to mathematically fit the physical sensor model of each subimage. Finally, the five SRFMs form a continuous PRFM, and the PRFM-based sensor orientation of the UVI images is performed. Three HaiYang-1C UVI images were tested. The experimental results showed that the unabsorbed geometric distortions fully propagated into the SRFM-based sensor orientation results. The orientation accuracy of the three images reached ∼7 pixels. In the PRFM-based sensor orientation, both the PRFM fitting errors and the inconsistent errors between the adjacent subimages could be negligible. The PRFM-based orientation accuracy was thereby noticeably improved and reached >1 pixel.
A study is presented concerning the performance of support vector machines (SVMs) and maximum likelihood classification (MLC) algorithms on texture features. A novel multivariate modeling method--partial least square regression (PLSR) is applied to obtain novel texture features from texture spectrum (TS). Three texture features, together with PLSR-combined TS features, are used in Brodatz texture classification tests. The experiments show: 1) SVM has higher classification precisions and better generalization abilities than MLC no matter what texture features used and more suits to small training set size (TSS) situations; 2) the new proposed feature combination method (PLSR) can greatly improve TS features discrimination ability for MLC, but not for SVM.
There are abundant metallic mineral resources on the sea floor, polymetallic nodule is an important one of them. A polymetallic nodule ore includes nickel, copper and manganese elements, etc. So the polymetallic nodule is very important and precious to industry. In order to know the distribution and reserves in the west and east pacific areas, a deep-tow optic system is imported from U.S. to acquire deep sea-floor images. Processing the images, we can extract some information and calculate some parameters: coverage, grain size and abundance, which stand for distribution and reserves of the polymetallic nodule. In the paper, features of the deep sea-floor image are analyzed, considering the characters, a processing procedure for the deep sea-floor pictures is presented. Methods are presented to rectify radiative uneven and geometric distortions, at last, the correlations of coverage, abundance and grain size are analyzed and the formulas for computing abundance are respectively derived from that.
This paper describes the use of semivariogram as a parameter for image comparison which is a commonly used method in content-based image retrieval. The authors first review various applications of spatial statistics to image and signal processing, and recent literature of image comparison, with the emphasis to global image structure description and distance-based image retrieval techniques. The difficulty arising in this field is the definition of image similarity. A new parameter based on semivariogram is putted forward by the authors. Bearing in mind that semivariogram is a parameter not only describes the global structure of a data set but also describes the local continuity of that data set, it is shown in the paper that semivariogram is suitable for global image comparison, and can be used to reveal local features of the image as well. Based on this property, a new index for image similarity is constructed and a practical program using it is developed. By applying the approach to a practical problem, the results show that this approach has the following merits: (a) high sensitivity to structure differences of an image. (b) low computational complexity, and (c) high robustness to lightening conditions.
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