A Randomized Subspace Learning Based Anomaly Detector for Hyperspectral Imagery

Limited by the existing imagery sensors, hyperspectral images are characterized by high spectral resolution but low spatial resolution. The super-resolution (SR) technique aiming at enhancing the spatial resolution of the input image is a hot topic in computer vision. In this paper, we present a hyperspectral image (HSI) SR method based on a deep information distillation network (IDN) and an intra-fusion operation. Specifically, bands are firstly selected by a certain distance and super-resolved by an IDN. The IDN employs distillation blocks to gradually extract abundant and efficient features for reconstructing the selected bands. Second, the unselected bands are obtained via spectral correlation, yielding a coarse high-resolution (HR) HSI. Finally, the spectral-interpolated coarse HR HSI is intra-fused with the input HSI to achieve a finer HR HSI, making further use of the spatial-spectral information these unselected bands convey. Different from most existing fusion-based HSI SR methods, the proposed intra-fusion operation does not require any auxiliary co-registered image as the input, which makes this method more practical. Moreover, contrary to most single-based HSI SR methods whose performance decreases significantly as the image quality gets worse, the proposal deeply utilizes the spatial-spectral information and the mapping knowledge provided by the IDN, which achieves more robust performance. Experimental data and comparative analysis have demonstrated the effectiveness of this method.

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution by Deep Spatial-Spectral Exploitation

Zhao

2019

“…The number of divided blocks K deciding the dimension of extracted feature is chosen from the set of {3, 5,10,15,20,25,30,35,40, 50} in sequence. Figure 10 presents the experimental result.…”

Section: Parameters Setting Discussionmentioning

confidence: 99%

“…Over the past decades, a large number of anomaly detection methods for hyperspectral images have been proposed [21][22][23][24][25]. They can be roughly divided into two categories, i.e., distribution hypothesis-based methods and geometric model-based methods, which will be introduced in detail as follows.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

Yuan

Wang

2019

A hyperspectral image usually covers a large scale of ground scene, which contains various materials with different spectral properties. When directly exploring the background information using all the image pixels, complex spectral interactions and inter-/intra-difference of different samples will significantly reduce the accuracy of background evaluation and further affect the detection performance. To address this problem, this paper proposes a novel hyperspectral anomaly detection method based on separability-aware sample cascade model. Through identifying separability of hyperspectral pixels, background samples are sifted out layer-by-layer according to their separable degrees from anomalies, which can ensure the accuracy and distinctiveness of background representation. First, as spatial structure is beneficial for recognizing target, a new spectral–spatial feature extraction technique is used in this work based on the PCA technique and edge-preserving filtering. Second, depending on different separability computed by sparse representation, samples are separated into different sets which can effectively and completely reflect various characteristics of background across all the cascade layers. Meanwhile, some potential abnormal targets are removed at each selection step to avoid their effects on subsequent layers. Finally, comprehensively taking different good properties of all the separability-aware layers into consideration, a simple multilayer anomaly detection strategy is adopted to obtain the final detection map. Extensive experimental results on five real-world hyperspectral images demonstrate our method’s superior performance. Compared with seven representative anomaly detection methods, our method improves the average detection accuracy with great advantages.

“…As a benefit from such rich spectral information, hyperspectral images (HSIs) have unique advantages for classification, detection, and recognition [3][4][5][6]. Real-time target detection aiming to find timely interesting targets has drawn much attention because of its significance in military and civilian applications [7][8][9]. Although the presence of targets in HSI provides critical information in method to update the operator matrix.…”

Section: Introductionmentioning

confidence: 99%

A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images

Lei

et al. 2019

Onboard target detection of hyperspectral imagery (HSI), considered as a significant remote sensing application, has gained increasing attention in the latest years. It usually requires processing huge volumes of HSI data in real-time under constraints of low computational complexity and high detection accuracy. Automatic target generation process based on an orthogonal subspace projector (ATGP-OSP) is a well-known automatic target detection algorithm, which is widely used owing to its competitive performance. However, ATGP-OSP has an issue to be deployed onboard in real-time target detection due to its iteratively calculating the inversion of growing matrices and increasing matrix multiplications. To resolve this dilemma, we propose a novel fast implementation of ATGP (Fast-ATGP) while maintaining target detection accuracy of ATGP-OSP. Fast-ATGP takes advantage of simple regular matrix add/multiply operations instead of increasingly complicated matrix inversions to update growing orthogonal projection operator matrices. Furthermore, the updated orthogonal projection operator matrix is replaced by a normalized vector to perform the inner-product operations with each pixel for finding a target per iteration. With these two major optimizations, the computational complexity of ATGP-OSP is substantially reduced. What is more, an FPGA-based implementation of the proposed Fast-ATGP using high-level synthesis (HLS) is developed. Specifically, an efficient architecture containing a bunch of pipelines being executed in parallel is further designed and evaluated on a Xilinx XC7VX690T FPGA. The experimental results demonstrate that our proposed FPGA-based Fast-ATGP is able to automatically detect multiple targets on a commonly used dataset (AVIRIS Cuprite Data) at a high-speed rate of 200 MHz with a significant speedup of nearly 34.3 times that of ATGP-OSP, while retaining nearly the same high detection accuracy. data analysis, it is rather rare and may be difficult to find because many of HSI-detected targets are relatively small and weak, such as anomalies [10]. Consequently, real-time target detection in HSI presents a great challenge and has become increasingly important.Currently, many algorithms have been developed for target detection in HSI, such as unsupervised fully constrained least squares (UFCLS) [11], automatic target-generation process based on an orthogonal subspace projector (ATGP-OSP) [12], and Reed-Xiaoli (RX) detector [13]. Among them, the performance of ATGP performs well in terms of detection accuracy and computational complexity according to the quantitative and comparative assessments [14,15]. However, despite the fact that automatic target detection has received increasing attention [16][17][18], two problems with real-time onboard applications need to be addressed. One is to continuously increase the scale of operations in the process of automatically iterating to update currently being found targets, which complicates the process of updating operator matrix. The other one is a large-scale matrix m...