A Bilateral Filter Based Post-Processing Approach for Supervised Spectral-Spatial Hyperspectral Image Classification

Abstract

To effectively improve the performance of representation based classifier, a spatial spectral joint classification post-processing approach is proposed, based on the application of edge preserving BF (Bilateral Filtering) method. The proposed framework includes two key processes: (1) the classifier (such as SRC, CRC, or KSRC) based on sparse representation of each pixel is used to obtain softclassified probabilities belonging to each information class for each pixel; (2) spatial spectral joint BF for the soft-classified probabilities map. It is aimed to integrate context-aware information for each pixel class labels. Under the spatial guidance image, extracted from the three principle component, a BF is employed to get the refined probability maps. The BF considers not only the spatial distance but it also considers the image context-aware distance which significantly improves the classification results. Finally, the class label is obtained by choosing the maximum probability criteria. The experimental results on three benchmark hyperspectral data sets showed that the "local smoothing" is efficient and has a potential to achieve high classification accuracy. All the algorithms are implemented with equal number of labeled samples and comparative results are presented in terms of visual classification map and numerical classification results. The major advantages of proposed method are: it is simple, noniterative and easy to implement. Hence, the advantages lead to significant usage in real applications.

Data Mining Approach for Detection and Classification of Brain Tumor

“…This paper proposes the edge preserving bilateral filtering-based [23] detection and classification method based on multiple steps. To test the accuracy of our proposed system, BRATS 2017 dataset has been used.…”

“…It operates in the domain and the range of the image. Bilateral filter has two kernel parameters: the range and the spatial kernel, these two kernels are used to measure the range and spatial distances between the neighbors with its center pixels [23]. These kernels are based on the Gaussian distribution as shown in Fig.…”

Hyperspectral image classification using a large selective kernel network hybridized to-kenization transformer

“…Noise interference is another important element that significantly affects the effectiveness of HSI classification and frequently results in the acquired images' quality declining. To mitigate this issue, certain edge-preserving filtering-based algorithms have been studied for HSI classification [10][11][12]. For instance, a new classification framework for classifying pixels is created by merging multiscale filtering and texture features, which can address the problems of texture detail, loss of a region's edge, and fixed extraction scale [12].…”