Jihao Yin

Using Hurst and Lyapunov Exponent For Hyperspectral Image Feature Extraction

Hyperspectral image processing has attracted high attention in remote sensing fields. One of the main issues is to develop efficient methods for dimensionality reduction via feature extraction. This letter proposes a new nonlinear unsupervised feature extraction algorithm using Hurst and Lyapunov exponents to reveal local and general spectral profiles, respectively. A hyperspectral reflectance curve from each pixel is regarded as a time series, and it is represented by Hurst and Lyapunov exponents. These two new features are then used to overcome the Hughes problem for reliable classification. Experimental results show that the proposed method performs better than a few other feature extraction methods tested.

Wavelet Packet Analysis and Gray Model for Feature Extraction of Hyperspectral Data

Wavelet packet analysis (WPA) and gray model (GM) are investigated for nonlinear unsupervised feature extraction of hyperspectral remote sensing data in this letter. Treated as derivative series, a hyperspectral response curve of each pixel is decomposed into an approximation and various detailed compositions by WPA, and then, GM is continuously applied to find the relationship among those detailed compositions. Cluster-space representation is used for determining the optimal wavelet. New extracted features can reveal the intrinsic identities of hyperspectral data. Experimental results show the feasibility and reliability of our proposed method in terms of classification accuracy.

Sparse Analysis Based on Generalized Gaussian Model for Spectrum Recovery With Compressed Sensing Theory

Imaging spectrometers can supply spatial data in abundant narrow and continuous wavelength bands. However, the huge data volume produced encounters the difficulty in storage and transmission. On the other hand, these hyperspectral data sets contain high redundancy, which offers an opportunity to reduce the number of spectral measurements and recover the full spectrum from limited samples without losing principal spectral information. This paper addresses the application of compressed sensing (CS) theory to hyperspectral data reconstruction. An important question involved is how to know a spectrum is sparse enough so that CS can be applied effectively. We provide a quantitative answer and develop a strategy to measure the degree of sparsity of a spectrum based on the generalized Gaussian distribution (GGD) model. The novelty includes the derivation of the sharpness of the GGD and how to estimate the sharpness of a spectral signal. The proposed strategy was tested using the spectral data from USGS database and an AVIRIS-HSI data set. The results demonstrate that it is important to introduce the sparsity measure, as CS offers a high reconstruction rate and low relative errors compared with the existing methods for sparse signals only.

Crater Detection Based on Gist Features

Craters are the most abundant landform on the planet surface, which could provide fundamental clues for planetary science. Due to variations in the terrain, illumination, and scale, it is challenging to detect craters through remote sensing images and it requires an effective crater feature extraction method. In this paper, we address this problem using Gist features, which can provide highly effective descriptions on craters local edges and global structure. The proposed crater detection procedure contains three key steps. First, we extract all candidate craters on a planet image using a boundary-based technique. Second, Gist features are generated from selected training samples. Third, crater detection is conducted using Gist feature vectors with random forest classification. Compared to pixel-based and Haar-like features, our method shows more accurate crater recognition, and achieves satisfied results in the experiments conducted on the Mars Orbiter Camera (MOC) database.

A New Target Detector for Hyperspectral Data Using Cointegration Theory

This paper introduces Cointegration Theory to address the problem of adaptive target detection in hyperspectral imagery. Cointegration Theory aims at mining a long-term equilibrium relationship, which refers to the condition that an appropriate linear combination of several non-stationary series can be stationary as long as they have similar or related drift. Hyperspectral response sequences, which are highly non-stationary, have similar patterns among the same materials. To be treated as a time series, each given hyperspectral curve is matched with the reference spectrum via the Johansen Cointegration Test. The statistic of the test is then used for target detection. Experimental results indicate that our proposed method is effective and has a strong capacity to identify interesting objects from their background.

Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning

Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is designed to extract the multiscale features from each superpixel and predict the superpixel as one of three classes including thick cloud, thin cloud, and noncloud. Finally, the predictions of all the superpixels in the image yield the cloud detection result. In the proposed cloud detection framework, the improved SLIC method can obtain accurate cloud boundaries by optimizing initial cluster centers, designing dynamic distance measure, and expanding search space. Moreover, different from traditional cloud detection methods that cannot achieve multilevel detection of cloud, the designed deep CNN model can not only detect cloud but also distinguish thin cloud from thick cloud. Experimental results indicate that the proposed method can detect cloud with higher accuracy and robustness than compared methods.

A novel method of crater detection on digital elevation models

As an essential geomorphological structures on planetary surface, impact craters can provide significant information in determining the planetary chronology. This paper proposes a novel automatic crater detection algorithm by using digital elevation model (DEM) data. The method includes: 1) pre-processing of the original DEM data, which can eliminate the effect of other landform; 2) iterative crater detections, which can eliminate small objects and analyze roundness. We use the DEMs instead of the imagery data because the DEMs could be unaffected by the solar altitude and atmospheric conditions, etc. Our detection algorithm is evaluated using several test sets of Martian DEM data obtained by the Mars Obiter Laser Altimeter (MOLA) boarded on the Mars Global Surveyor. The experimental results show the high true detection rate and low false detection rate of our algorithm according to the Barlow catalogue.

Crater detection based on local non-negative matrix factorization

Due to the variations in the terrain, illumination and scale, it is difficult to detect craters from remote sensing image of planet surface. This paper proposes a novel automatic crater detection method by introducing the local non-negative matrix factorization (LNMF) for remote sensing images of Martian surface. LNMF is aimed at learning localized, part-based features from global samples, which has shown considerable prospect in feature extraction. Our detection algorithm contains three key procedures. Firstly, the crater candidates are detected by geometry approaches. Secondly, LNMF is applied in subspace learning for all crater samples and candidates. At last, we get the final detection results by discarding non-craters in candidates. The LNMF-based method has achieved satisfied results in the experiments conducted on the Mars Orbiter Camera (MOC) dataset.

Cointegration theory for adaptive target detection in hyperspectral images

This paper investigates the usage of Johansen Cointegration Test for adaptive target detection with hyperspectral remote sensing data. Johansen Cointegration Test aims at mining long-term equilibrium relationship, which refers to the condition that if pairs of non-stationary series share similar tendencies, their linear combination could be stationary. Hyperspectral data are highly non-stationary series, but there should be similar patterns among the hyperspectral response curves of same materials. To be treated as derivative series, given hyperspectral response curves will be matched with the standard spectrum via Johansen Cointegration Test. The test statistics will be compared to a preset threshold to judge whether they are target or not. Quantitative experiments show that the proposed method performs better than a few other adaptive detection methods tested.

Hybrid Norm Pursuit Method for Hyperspectral Image Reconstruction

This paper proposes a hybrid I0 and I1 norm pursuit (HNP) method for reconstructing hyperspectral image with high speed and high fidelity. The HNP method provides an approximate result by a simple and fast I0 norm algorithm [such as the orthogonal matching pursuit (OMP)] first and then regulates it to an accurate result by a good but slow I1 norm algorithm [such as the gradient projection for sparse reconstruction (GPSR)]. We build a mathematic model for the HNP method and formulate it to be a constraint optimization problem. How to choose the best switch point is investigated to ensure that the HNP method is able to provide the best reconstruction performance. Experimental results demonstrate that the HNP method is fast and offers high accuracy for hyperspectral image reconstruction and classification.