Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection

In recent years, multi-view subspace learning has been garnering increasing attention. It aims to capture the inner relationships of the data that are collected from multiple sources by learning a unified representation. In this way, comprehensive information from multiple views is shared and preserved for the generalization processes. As a special branch of temporal series hyperspectral image (HSI) processing, the anomalous change detection task focuses on detecting very small changes among different temporal images. However, when the volume of datasets is very large or the classes are relatively comprehensive, existing methods may fail to find those changes between the scenes, and end up with terrible detection results. In this paper, inspired by the sketched representation and multi-view subspace learning, a sketched multi-view subspace learning (SMSL) model is proposed for HSI anomalous change detection. The proposed model preserves major information from the image pairs and improves computational complexity by using a sketched representation matrix. Furthermore, the differences between scenes are extracted by utilizing the specific regularizer of the self-representation matrices. To evaluate the detection effectiveness of the proposed SMSL model, experiments are conducted on a benchmark hyperspectral remote sensing dataset and a natural hyperspectral dataset, and compared with other state-of-the art approaches

An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification

Accurate vegetation detection is important for many applications, such as crop yield estimation, landcover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA)

An Experimental Study for the Effects of Noise on Hyperspectral Imagery Classification

Hyperspectral image (HSI) classification is a very important topic in remote sensing. There are many published methods for HSI classification in the literature. Nevertheless, it is not clear which method is the most robust to noise in HSI data cubes. In this paper, we conduct a systematic study to examine the effects of noise in HSI data cubes on classification methods. We compare ten existing methods for HSI classification when Gaussian white noise (GWN) and shot noise are present in the HSI data cubes. We have figured out which method is the most robust to GWN and shot noise respectively by experimenting on three widely used HSI data cubes. We have also measured the CPU computational time of every method compared in this paper for HSI classification

MDLatLRR: A novel decomposition method for infrared and visible image fusion

Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent low-rank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We develop a novel image fusion framework based on MDLatLRR, which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts, and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.Comment: IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 table

A Deep Decomposition Network for Image Processing: A Case Study for Visible and Infrared Image Fusion

Image decomposition is a crucial subject in the field of image processing. It can extract salient features from the source image. We propose a new image decomposition method based on convolutional neural network. This method can be applied to many image processing tasks. In this paper, we apply the image decomposition network to the image fusion task. We input infrared image and visible light image and decompose them into three high-frequency feature images and a low-frequency feature image respectively. The two sets of feature images are fused using a specific fusion strategy to obtain fusion feature images. Finally, the feature images are reconstructed to obtain the fused image. Compared with the state-of-the-art fusion methods, this method has achieved better performance in both subjective and objective evaluation

Electronic countermeasures applied to passive radar

Passive Radar (PR) is a form of bistatic radar that utilises existing transmitter infrastructure such as FM radio, digital audio and video broadcasts (DAB and DVB-T/T2), cellular base station transmitters, and satellite-borne illuminators like DVB-S instead of a dedicated radar transmitter. Extensive research into PR has been performed over the last two decades across various industries with the technology maturing to a point where it is becoming commercially viable. Nevertheless, despite the abundance of PR literature, there is a scarcity of open literature pertaining to electronic countermeasures (ECM) applied to PR. This research makes the novel contribution of a comprehensive exploration and validation of various ECM techniques and their effectiveness when applied to PR. Extensive research has been conducted to assess the inherent properties of the lluminators of Opportunity to identify their possible weaknesses for the purpose of applying targeted ECM. Similarly, potential jamming signals have also been researched to evaluate their effectiveness as bespoke ECM signals. Whilst different types of PR exist, this thesis focuses specifically on ECM applied to FM radio and DVB-T2 based PR. The results show noise jamming to be effective against FM radio based PR where jamming can be achieved with relatively low jamming power. A waveform study is performed to determine the optimal jamming waveform for an FM radio based PR. The importance of an effective direct signal interference (DSI) canceller is also shown as a means of suppressing the jamming signal. A basic overview of counter-ECM (ECCM) is discussed to counter potential jamming of FM based PR. The two main processing techniques for DVB-T2 based PR, mismatched and inverse filtering, have been investigated and their performance in the presence of jamming evaluated. The deterministic components of the DVB-T2 waveform are shown to be an effective form of attack for both mismatched filtering and inverse filtering techniques. Basic ECCM is also presented to counter potential pilot attacks on DVB-T2 based PR. Using measured data from a PR demonstrator, the application and effectiveness of each jamming technique is clearly demonstrated, evaluated and quantified