Change Detection In Hyperspectral Images Research Articles

Unsupervised Hyperspectral Image Change Detection via Deep Learning Self-Generated Credible Labels

Change detection (CD) aims to identify differences in scenes observed at different times. Hyperspectral image (HSI) is preferred for the understanding of land surface changes, since it can provide essential and unique features for CD. However, due to the high-dimensionality and limited data, the HSI-CD task is challenged. While model-driven CD methods are hard to achieve high accuracy due to the weak detection performance for fine changes, data-driven CD methods are hard to be generalized due to the limited datasets. The state-of-art method is to combine a single model-driven method with a data-driven convolutional neural network (CNN). Wherein the pseudolabels can be generated automatically by the model-driven method and then fed to CNN for training. However, the final detection accuracy is limited by the model-driven method which produces pseudolabels with one-sidedness and low accuracy. Therefore, the generation of credible pseudolabels is anticipated and crucial for such a combination. Herein, a novel strategy, the combination of two complementary model-driven methods, structural similarity (SSIM) and change vector analysis (CVA), is proposed to generate credible labels for training a subsequent CNN. The results show that the final accuracy is higher than that of SSIM and CVA. The main contributions of this article are threefold: First, a new paradigm for generating credible labels is proposed. Second, SSIM is used for the first time for HSI-CD tasks. Third, an unsupervised end-to-end framework is presented for the HSI-CD. Experimental results demonstrate the effectiveness of the proposed framework.

TDSSC: A Three-Directions Spectral–Spatial Convolution Neural Network for Hyperspectral Image Change Detection

Change detection (CD) is a hot issue in the research of remote sensing technology. Hyperspectral images (HSIs) greatly promote the development of CD technology because of their high resolution in the spectral domain. However, some traditional CD methods currently applied to low-dimensional and multispectral images cannot adapt to the complex high-dimensional features of the HSIs. In addition, the spectral measurements of the HSI contain a lot of noise and redundancy, which greatly contaminates spectral-only information for CD. In order to fully extract the discriminant features of HSI to improve the accuracy of CD, this article proposes a three-directions spectral–spatial convolution neural network (TDSSC). A novel method for three-direction decomposition of hyperspectral change tensors is proposed—change tensor is decomposed along the spectral direction and two spatial directions to get a single tensor containing the spectral information and two kinds of tensors containing the spectral–spatial information. TDSSC uses 1-D convolution to extract spectral features from the spectral direction as well as reducing the tensor dimension, which helps the latter network to be lightweight and significantly improves the speed of change detection. Also, it uses 2-D convolution to extract spectral–spatial features from two spatial directions of the reduced tensor, and to extract features from different directions to improve the accuracy and Kappa value of CD. The experimental results of three real hyperspectral datasets show that TDSSC is superior to most existing CD methods.

A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges

The expected increasing availability of remote sensing satellite hyperspectral (HS) images provides an important and unique data source for Earth observation (EO). HS images are characterized by a detailed spectral sampling (i.e., very high spectral resolution) over a wide spectral wavelength range, which makes it possible to monitor land-cover dynamics at a fine spectral scale. This is due to its capability of detecting subtle spectral variations in multitemporal images associated with land-cover changes that are not detectable in traditional multispectral (MS) images because of their limited spectral resolution (i.e., sufficient for representing only abrupt, strong changes in the spectral signature, as a rule). To fully exploit the available multitemporal HS images and their rich information content in change detection (CD), it is necessary to develop advanced automatic techniques that can address the complexity of the extraction of change information in an HS space. This article provides a comprehensive overview of the CD problem in HS images, as well as a survey on the main CD techniques available for multitemporal HS images. We review both widely used methods and new techniques proposed in the recent literature. The basic concepts, categories, open issues, and challenges related to CD in HS images are discussed and analyzed in detail. Experimental results obtained using state-of-the-art approaches are shown, to illustrate relevant concepts and problems.

GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with the highspectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for the objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts.

Introductory view of anomalous change detection in hyperspectral images within a theoretical gaussian framework

Exploitation of temporal series of hyperspectral images is a relatively new discipline that has gained a lot of attention from the image processing scientific community. In this paper, we consider the specific problem of anomalous change detection (ACD) in hyperspectral images and discuss how images taken at two different times can be processed to detect changes caused by insertion, deletion, or displacement of small objects in the monitored scene. We introduce the ACD problem using an approach based on the statistical decision theory and we derive a common framework including different ACD approaches. Far from being inclusive of all the methods proposed in the literature, this tutorial overview places emphasis on techniques based on the multivariate Gaussian model that allows a formal presentation of the ACD problem and the rigorous derivation of the possible solutions in a way that is both mathematically more tractable and easier to interpret. The unification of different approaches under a single rigorous statistical scheme provides both a tutorial overview of ACD techniques, and a useful instrument for researchers already familiar with the ACD problem. Dedicated preprocessing methods aimed at improving the robustness of the ACD process are also discussed. Real data are exploited to test and compare the presented methods, highlighting advantages and drawbacks of each approach. The tutorial aspect of the paper has suggested the use of a freely available data set. This should hopefully motivate the interested reader to experiment with the processing methods and performance evaluation chain presented herein.