Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification

Synthetic aperture radar (SAR) image classification is an important task in remote sensing applications. However, it is challenging due to the speckle embedding in SAR imaging, which significantly degrades the classification performance. To address this issue, a new SAR image classification framework based on multi-feature fusion and adaptive kernel combination is proposed in this paper. Expressing pixel similarity by non-negative logarithmic likelihood difference, the generalized neighborhoods are newly defined. The adaptive kernel combination is designed on them to dynamically explore multi-feature information that is robust to speckle noise. Then, local consistency optimization is further applied to enhance label spatial smoothness during classification. By simultaneously utilizing adaptive kernel combination and local consistency optimization for the first time, the texture feature information, context information within features, generalized spatial information between features, and complementary information among features is fully integrated to ensure accurate and smooth classification. Compared with several state-of-the-art methods on synthetic and real SAR images, the proposed method demonstrates better performance in visual effects and classification quality, as the image edges and details are better preserved according to the experimental results.

Spatially-encouraged spectral clustering: a technique for blending map typologies and regionalization

Clustering is a central concern in geographic data science and reflect a large, ongoing domain of research. In applied problems, it is often challenging to balance the two notions of coherence in spatial clustering problems: that of "feature" coherence, where detected clusters are internally homogeneous, and "spatial'" coherence, where detected clusters can be interpreted to represent a geographical place. While recent work has aimed to relax this tension, progress in spectral clustering methods, developed for machine learning and image segmentation, provide a useful framework to do this. This paper shows how spatial and feature coherence can be balanced using kernel combination in spectral clustering. This ensures the preservation of geographical constraints (like contiguity or compactness) while also providing the ability to relax these constraints linearly. Further, some kinds of kernel combination methods have significantly different behavior and meaning from another commonly-used method to balance objectives: convex combination. Altogether, spatially-encouraged spectral clustering is proposed as a novel spatial analysis method that bridges regionalization and spatial clustering.

Multiple Indefinite Kernel Learning for Feature Selection

Multiple kernel learning for feature selection (MKL-FS) utilizes kernels to explore complex properties of features and performs better in embedded methods. However, the kernels in MKL-FS are generally limited to be positive definite. In fact, indefinite kernels often emerge in actual applications and can achieve better empirical performance. But due to the non-convexity of indefinite kernels, existing MKL-FS methods are usually inapplicable and the corresponding research is also relatively little. In this paper, we propose a novel multiple indefinite kernel feature selection method (MIK-FS) based on the primal framework of indefinite kernel support vector machine (IKSVM), which applies an indefinite base kernel for each feature and then exerts an l1-norm constraint on kernel combination coefficients to select features automatically. A two-stage algorithm is further presented to optimize the coefficients of IKSVM and kernel combination alternately. In the algorithm, we reformulate the non-convex optimization problem of primal IKSVM as a difference of convex functions (DC) programming and transform the non-convex problem into a convex one with the affine minorization approximation. Experiments on real-world datasets demonstrate that MIK-FS is superior to some related state-of-the-art methods in both feature selection and classification performance.