Pradyut Kumar Biswal

Superpixel segmentation is a potential preprocessing tool that can simplify an image by creating semantic sub-regions of the image. This is quite effective in reducing the misclassification of isolated pixels. Image segmentation using superpixels has been successfully applied in the computer vision field. Recently, superpixel segmentation is being adopted into HSI processing domain also, due to its superior properties. In this work, for the accurate classification of the Hyperspectral Image, a texture-based superpixel segmentation algorithm is proposed. Local Binary Pattern (LBP) and Gabor filters are first incorporated to extract the local and global image texture information. Next, Simple Linear Iterative Clustering (SLIC) superpixel segmentation algorithm is applied over the extracted texture features and a superpixel segmentation map is obtained. Finally, majority voting strategy is performed between the superpixel segmentation map and the pixel-wise classification map to acquire the final classification map. For validating the effectiveness of the proposed algorithm, experiments were conducted on four popular HSI datasets, namely: Indian Pines, Pavia University, Houston 2013, and Houston 2018 datasets. Superior classification performance was observed by the proposed method in comparison to other state-of-the-art methods.

In the processing of remotely sensed data, classification may be preceded by feature extraction, which helps in making the most informative parts of the data emerge. Effective feature extraction may boost the efficiency and accuracy of the following classification, and hence various methods have been proposed to perform it. Recently, Singular Spectrum Analysis (SSA) and its 2-D variation (2D-SSA) have emerged as popular, cutting-edge technologies for effective feature extraction in Hyperspectral Images (HSI). Using 2D-SSA, each band image of an HSI is initially decomposed into various components, and then the image is reconstructed using the most significant eigen-tuples relative to their eigen-values, which represent strong spatial features for the classification task. However, instead of performing reconstruction on the whole image, it may be more effective to apply reconstruction to object-specific spatial regions, which is the proposed objective of this research. As an HSI may cover a large area, multiple objects are generally present within a single scene. Hence, spatial information can be highlighted accurately by specializing the reconstruction based on the local context. The local context may be defined by the so-called superpixels, i.e., finite sets of pixels that constitute a homogeneous set. Each superpixel may undergo tailored reconstruction, with a process expected to perform better than non-spatially-adaptive approaches. In this paper, a Superpixel-based SSA (SP-SSA) method is proposed where the image is first segmented into multiple regions using a superpixel segmentation approach. Next, each segment is individually reconstructed using 2D-SSA. In doing so, the spatial contextual information is preserved, leading to better classifier performance. The performance of the reconstructed features is evaluated using an SVM classifier. Experiments on four popular benchmark datasets reveal that, in terms of the classification accuracy, the proposed approach overperforms the standard SSA technique and various common spatio-spectral classification methods.

The objective of this paper is to develop an efficient noise detector and filter for random valued impulse noise (RVIN) in gray level images. A Dual Histogram based Noise Detector (DHND) is proposed and compared with other approaches using the classical measures like False Alarm (FA) and Miss Detection (MD). Also a novel filtering approach is proposed namely Hybrid Gaussian Filtering (HGF) which combines two filters such as Gaussian Impulse Kernel (GIK) and Gaussian Weighted Median (GWM). The proposed HGF approach is validated across state-of-art filters in terms of standard performance measures such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Feature Similarity Index Measure (FSIM). The robustness of the filter is experimented for various standard gray scale images to evaluate its global significance. Experimental results reveal the pros and cons of other algorithms along with the effectiveness of the proposed filter under varying noise conditions.

Recent developments in hyperspectral sensors have made it possible to acquire HyperSpectral Images (HSI) with higher spectral and spatial resolution. Hence, it is now possible to extract detailed information about relatively smaller structures. Despite these advantages, HSI suffers from many challenges also, like higher spatial variability of spectral signatures, the Hughes effect due to higher dimensionality, and a limited number of labeled training samples compared to the dimensions of the spectral space. Superpixels can be a potentially effective tool in tackling these challenges. Superpixel segmentation is a process of segmenting the spatial image into several semantic sub-regions with similar characteristic features. Such grouping by similarity can significantly ease the subsequent processing steps. Because of this, superpixels have been successfully applied to various fields of HSI processing like Classification, Spectral Unmixing, Dimensionality Reduction, Band Selection, Active Learning, Denoising, and Anomaly Detection. This paper focuses on Classification, presenting a detailed survey of superpixel segmentation approaches for the classification of HSI. The Superpixel creation algorithm framework and post-processing frameworks for superpixels in HSI are also analyzed. Also, a brief description of various application areas of superpixels is provided. An experimental analysis of existing superpixel segmentation approaches is also provided in this paper, supported by quantitative results on standard benchmark datasets. The challenges and future research directions for the implementation of superpixel algorithms are also discussed.

A hyperspectral image (HSI) is a collection of several narrow-band images that span a wide spectral range. Each band reflects the same scene, composed of various objects imaged at different wavelengths; the spatial information, however, remains generally consistent across bands. Both types of information, spectral and spatial, can be leveraged to identify and classify objects. Recently, the use of machine learning (ML) in object classification has become increasingly widespread. Regardless of the selected approach, object-specific spectral and spatial information is key to discriminating relevant categories. Whereas spatial information is usually repeated across bands, spectral information tends to be distributed more unevenly and often highly so. This poses the issue of removing redundancy, which is commonly called the band selection ( BS ) problem and refers to identifying an optimal subset of bands for further HSI processing.