scholarly journals STACKED LOCAL FEATURE DETECTOR FOR HYPERSPECTRAL IMAGE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 495-500
Author(s):  
Z. Yan ◽  
Z. Wu
Keyword(s):  
Image Matching ◽  
Hyperspectral Image ◽  
Spectral Band ◽  
Local Feature ◽  
Feature Detector ◽  
Hyperspectral Image Processing ◽  
Scan Line ◽  
Accuracy Increase ◽  
Matching Score ◽  
Almost All

Abstract. Images registration is an important task in hyperspectral image processing, while almost all local feature point based image matching algorithm is designed for single band image only and miss the advantage of abundant spectral information. Therefor, in this paper we propose a novel local feature detector for hyperspectral image. This method, which is named stacked local feature detector (HSI-SFD), stack all local feature points detected from every single spectral band. With redundant information, local feature miss-detected at noise pixel can be filtered out by the stack count threshold, leading to more reliable and robust local features. To verify the algorithm, a hyperspectral image matching dataset is built. Multiview hyperspectral images of the several different flat targets are taken by scan-line hyperspectral camera in library. Each image contains 270 bands within 400–1000 nm. Groundtruth transformation matrix between images is computed from corresponding points selected by hands. Experiment on the dataset shows that features points miss-detected at texture-less area can be inhibited by HSI-SFD. Both keypoint repeatability and matching accuracy increase significantly. Precision and matching score increase up to 10% for some scene.

scholarly journals A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3627 ◽  
Author(s):  
Yi Zhang ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yan Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Sparse Representation ◽  
Parallel Algorithm ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Detection Methods ◽  
Matrix Computations ◽  
Hyperspectral Image Processing ◽  
Mapreduce Model

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

scholarly journals Sparse Feature Learning of Hyperspectral Imagery via Multiobjective-Based Extreme Learning Machine

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1262 ◽  
Author(s):  
Xiaoping Fang ◽  
Yaoming Cai ◽  
Zhihua Cai ◽  
Xinwei Jiang ◽  
Zhikun Chen
Keyword(s):  
Multiobjective Optimization ◽  
Extreme Learning Machine ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Band ◽  
Feature Learning ◽  
Pareto Solution ◽  
Conflicting Objectives ◽  
Learning Machine ◽  
Hidden Layer

Hyperspectral image (HSI) consists of hundreds of narrow spectral band components with rich spectral and spatial information. Extreme Learning Machine (ELM) has been widely used for HSI analysis. However, the classical ELM is difficult to use for sparse feature leaning due to its randomly generated hidden layer. In this paper, we propose a novel unsupervised sparse feature learning approach, called Evolutionary Multiobjective-based ELM (EMO-ELM), and apply it to HSI feature extraction. Specifically, we represent the task of constructing the ELM Autoencoder (ELM-AE) as a multiobjective optimization problem that takes the sparsity of hidden layer outputs and the reconstruction error as two conflicting objectives. Then, we adopt an Evolutionary Multiobjective Optimization (EMO) method to solve the two objectives, simultaneously. To find the best solution from the Pareto solution set and construct the best trade-off feature extractor, a curvature-based method is proposed to focus on the knee area of the Pareto solutions. Benefited from the EMO, the proposed EMO-ELM is less prone to fall into a local minimum and has fewer trainable parameters than gradient-based AEs. Experiments on two real HSIs demonstrate that the features learned by EMO-ELM not only preserve better sparsity but also achieve superior separability than many existing feature learning methods.

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