Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been recently proposed for feature extraction in hyperspectral remote sensing. With the help of hidden nodes in deep layers, a high-level abstraction is achieved for data reduction whilst maintaining the key information of the data. As hidden nodes in SAEs have to deal simultaneously with hundreds of features from hypercubes as inputs, this increases the complexity of the process and leads to limited abstraction and performance. As such, segmented SAE (S-SAE) is proposed by confronting the original features into smaller data segments, which are separately processed by different smaller SAEs. This has resulted in reduced complexity but improved efficacy of data abstraction and accuracy of data classification

Effective denoising and classification of hyperspectral images using curvelet transform and singular spectrum analysis

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet transformed domain via a relatively new spectral feature processing technique – singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine (SVM) classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracies over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artefacts introduced during the data acquisition process as well. By adding an extra spatial post-processing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral-spatial classification methods

SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers

Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Owing to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective with transformers, and propose a novel backbone network called \ul{SpectralFormer}. Beyond band-wise representations in classic transformers, SpectralFormer is capable of learning spectrally local sequence information from neighboring bands of HS images, yielding group-wise spectral embeddings. More significantly, to reduce the possibility of losing valuable information in the layer-wise propagation process, we devise a cross-layer skip connection to convey memory-like components from shallow to deep layers by adaptively learning to fuse "soft" residuals across layers. It is worth noting that the proposed SpectralFormer is a highly flexible backbone network, which can be applicable to both pixel- and patch-wise inputs. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with state-of-the-art backbone networks. The codes of this work will be available at https://github.com/danfenghong/IEEE_TGRS_SpectralFormer for the sake of reproducibility

Explainable contextual data driven fusion

Numerous applications require the intelligent combining of disparate sensor data streams to create a more complete and enhanced observation in support of underlying tasks like classification, regression, or decision making. This presentation is focused on two underappreciated and often overlooked parts of information fusion, explainability and context. Due to the rapidly increasing deployment and complexity of machine learning solutions, it is critical that the humans who deploy these algorithms can understand why and how a given algorithm works, as well as be able to determine when an algorithm is suitable for use in a particular instance of the problem. The first half of this paper outlines a new similarity measure for capacities and integrals. This measure is used to compare machine learned fusion solutions and explain what a single fusion solution learned. The second half of the paper is focused on contextual fusion with respect to incomplete (limited knowledge) models and metadata for unmanned aerial vehicles (UAVs). Example UAV metadata includes platform (e.g., GPS, IMU, etc.) and environmental (e.g., weather, solar position, etc.) data. Incomplete models herein are a result of limitations of machine learning related to under-sampling of training data. To address these challenges, a new contextually adaptive online Choquet integral is outlined

Radar Sensing in Assisted Living: An Overview

This paper gives an overview of trends in radar sensing for assisted living. It focuses on signal processing and classification, looking at conventional approaches, deep learning and fusion techniques. The last section shows examples of classification in human activity recognition and medical applications, e.g. breathing disorder and sleep stages recognition