A New Search Algorithm for Feature Selection in Hyperspectral Remote Sensing Images

A new suboptimal search strategy suitable for feature selection in very high-dimensional remote-sensing images (e.g. those acquired by hyperspectral sensors) is proposed. Each solution of the feature selection problem is represented as a binary string that indicates which features are selected and which are disregarded. In turn, each binary string corresponds to a point of a multidimensional binary space. Given a criterion function to evaluate the effectiveness of a selected solution, the proposed strategy is based on the search for constrained local extremes of such a function in the above-defined binary space. In particular, two different algorithms are presented that explore the space of solutions in different ways. These algorithms are compared with the classical sequential forward selection and sequential forward floating selection suboptimal techniques, using hyperspectral remote-sensing images (acquired by the AVIRIS sensor) as a data set. Experimental results point out the effectiveness of both algorithms, which can be regarded as valid alternatives to classical methods, as they allow interesting tradeoffs between the qualities of selected feature subsets and computational cost

Sparse Coding Based Feature Representation Method for Remote Sensing Images

In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit (SOMP) and image fusion and recursive filtering (IFRF). The results from the experiments showed that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification. To further verify the power of the new feature representation method, we applied it to a pan-sharpened image to detect seafloor scars in shallow waters. Propeller scars are formed when boat propellers strike and break apart seagrass beds, resulting in habitat loss. We developed a robust identification system by incorporating morphological filters to detect and map the scars. Our results showed that the proposed method can be implemented on a regular basis to monitor changes in habitat characteristics of coastal waters