Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite ...remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.
For the detection of changes, several statistical techniques exist. When adopted to high-resolution imagery, the results of traditional pixel-based algorithms are often limited. We propose an ...unsupervised change detection and classification procedure based on object features. Following the automatic pre-processing of the image data, image objects and their object features are extracted. Change detection is performed by the multivariate alteration detection (MAD), accompanied by the maximum autocorrelation factor (MAF) transformation. The change objects are then classified using the fuzzy maximum likelihood estimation (FMLE). Finally the classification of changes is improved by probabilistic label relaxation.
A human observer can easily categorize an image into classes of interest but it is generally difficult to reproduce the same result using a computer. The emerging object-based methodology for image ...classification appears to be a better way to mimic the human thought process. Unlike pixel-based techniques which only use the layer pixel values, the object-based techniques can also use shape and context information of a scene texture. These extra degrees of freedom provided by the objects will aid the identification (or classification) of visible textures. However, the concept of image-objects brings with it a large number of object features and thus a lot of information is associated with the objects. In this article, we present a procedure for object-based classification which effectively utilizes the huge information associated with the objects and automatically generates classification rules. The solution of automation depends on how we solve the problem of identifying the features that characterize the classes of interest and then finding the final distribution of the classes in the identified feature space. We try to illustrate the procedure applied for a two-class case and then suggest possible ways to extend the method for multiple classes.
We describe the performance of a $\mathrm{23\times 23\times30 ~mm^3}$ low
background cerium bromide, CeBr$_3$(LB), scintillator crystal coupled to a
Hamamatsu R11265U-200 photomultiplier. This ...detector will be the building block
for a gamma-ray detector array designed to be the payload for a CubeSat to be
launched in 2020. The aim of the mission is to study flashes of gamma rays of
terrestrial origin. The design of the detector has been tuned for the detection
of gamma rays in the 20 keV$-$3 MeV energy range.
In change detection analysis, the computation of the no-change distribution is affected when changed pixels are in large number in the scene. Because of this, the performance of several techniques ...are compromised. In this paper we compare two well known automatic change detection techniques (ITPCA and IRMAD) by performing an initial elimination of the strong changes in order to minimize the contribution of the changed pixels to the radiometric normalization computation. These two techniques are ineffective in correctly estimating the distribution of the no-change pixels when this kind of scenario is encountered. The strong changes are identified by building an initial change mask (ICM), which is based on the statistical analysis of the given data set. In this paper we show two simple algorithms for building the ICM. From the experiments on a data set characterized by a high amount of changes due to the agriculture activity, the improvement in quality of the map of changes obtained by the proposed approach with respect to the ones obtained without using the ICM has been observed.
Recently, morphological profiles have be observed as good tools to fuse spectral and spatial information to produce better classification results. In general, the profiles are built with the features ...derived using the principal component analysis (PCA). Auto-associative neural network (AANN), which can be seen as an implementation of non-linear PCA is used for unsupervised feature reduction of hyperspectral data. In this paper, we investigate the suitability of the features derived using AANN to build extended morphological profiles for hyperspectral data classification.
A method for automatic identification of changes using regression with neural networks is presented. The regression is iteratively performed by updating the weights of the pixels. The method is ...applied to a small subset of two Landsat images and the results indicate that the proposed method produces good results.
Dealing with a high number of features belonging to different types of data such as Hyperspectral image and Morphological Attribute Profiles (MAPs) might lead to a poor predictive performance of the ...classifier and hence low final accuracies of classification. This is due to the Hughes effect that consistently decreases the power of prediction of the classifier, in case of a limited and fixed number of training samples. In order to reduce the number of features and only keeping those which are more informative, a novel supervised feature selection technique based on GAs and the measure of the relevance of the features is presented in this work. Moreover, the effectiveness of the proposed technique was demonstrated by experimenting on an optical remote sensed dataset.
We describe the performance of a \(\mathrm{23\times 23\times30 ~mm^3}\) low background cerium bromide, CeBr\(_3\)(LB), scintillator crystal coupled to a Hamamatsu R11265U-200 photomultiplier. This ...detector will be the building block for a gamma-ray detector array designed to be the payload for a CubeSat to be launched in 2020. The aim of the mission is to study flashes of gamma rays of terrestrial origin. The design of the detector has been tuned for the detection of gamma rays in the 20 keV\(-\)3 MeV energy range.
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