Annual westward propagating anomalies near 26 degrees N and eddy generation south of the Canary Islands: remote sensing (altimeter/SeaWIFS) and in situ measurement

Seasonal changes in altimeter data are derived for the North Atlantic Ocean. Altimeter data are then used to examine annually propagating structure along 26 degree N. By averaging the altimeter data into monthly values or by Fourier analysis, a positive anomaly can be followed from 17 degree W to similar to 50 degree W along similar to 26 degree N. The methods give a westward travel speed of 1 degree of longitude a month and a half-life of one year for the average decaying structure. At similar to 50 degree W 26 degree N, the average structure is about 2.8 years old with an elevation signal of similar to 1 cm, having gravelled similar to 3300 km westward. The mean positive anomaly results from the formation of anticyclonic eddies which are generally formed annually south of the Canary Islands by late summer and which then travel westward near 26 degree N. Individual eddy structure along 26 degree N is examined and related to in situ measurements and anomalies in the annual seasonal concentration cycle of SeaWiFS chlorophyll-a.

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.