07-10-2016, 03:04 PM
Automatic Change Analysis in Satellite Images Using Binary Descriptors and Lloyd–Max Quantization
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Abstract—In this letter, we present a novel technique for un-supervised change analysis that leads to a method of ranking the changes that occur between two satellite images acquired at dif-ferent moments of time. The proposed change analysis is based on binary descriptors and uses the Hamming distance as a similarity metric. In order to render a completely unsupervised solution, the obtained distances are further classified using vector quantization methods (i.e., Lloyd’s algorithm for optimal quantization). The ul-timate goal in the change analysis chain is to build change intensity maps that provide an overview of the severeness of changes in the area under analysis. In addition, the proposed analysis technique can be easily adapted for change detection by selecting only two levels for quantization. This discriminative method (i.e., between changed/unchanged zones) is compared with other previously de-veloped techniques that use principal component analysis or Bayes theory as starting points for their analysis. The experiments are carried on Landsat images at a 30-m spatial resolution, covering an area of approximately 59 × 51 km2 over the surroundings of Bucharest, Romania, and containing multispectral information.
. INTRODUCTION
THE past few years have witnessed an increased interest toward unsupervised change detection techniques. Unlike supervised methods, the latter techniques directly compare two multitemporal images, without having any other type of infor-mation regarding the contained classes or a priori distribution of the change/unchanged states. Under these assumptions, a challenging step is to find the optimal threshold to discriminate
between change and no change.
The majority of the change detection methods are developed based on the analysis of the difference image. Other techniques
use change vector analysis to represent the change in the n-dimensional spectral space and to show that each class of change has a distinct spectral signature [1].
In [2], two techniques based on the Bayes theory are pro-posed for the analysis of the difference image. The first ap-proach assumes that the pixels in the difference image are independent of one another and computes the optimal threshold by minimizing the error probability, which, in Bayesian terms, translates into maximizing the posterior conditional probabil-ity. The second technique uses Besag’s iterated conditional modes for solving a Markov Random Field (MRF)-based model, which considers that a pixel belonging to a certain class (change/no change) is likely to be surrounded by pixels belonging to the same class. Although the spatial contextual hypothesis is likely to be true in most cases, the problem of convergence becomes problematic when changes appear in too many scattered places. However, the exploitation of contextual information with MRF models is not well suited for online applications due to their high computational complexity. Both techniques are based on the estimation of statistical parameters using the expectation–maximization (EM) algorithm.
Other approaches consist of linear transformations [3]. For example, principal component analysis (PCA) is applied in [4] over nonoverlapping blocks of the difference image in order to extract the main directions of change. In the same letter, the challenging problem of searching the threshold between change and no change is solved by introducing a K-means step
(K = 2).
In this letter, we propose a multilevel change detection method to assess the degree of change suffered by an area between two moments of time. The system provides a fast and unsupervised diagnosis of change that does not require the availability of any type of a priori information or any learning phase. To this end, we start our analysis from two images acquired at different moments of time over the same area. For each pixel inside each image, we build a binary descriptor rep-resenting the gradients in the neighborhood around that pixel. Next, the similarity for each pixel is measured by applying the Hamming distance over the two temporal binary descriptors computed at the same location. Finally, Lloyd–Max’s algorithm is used to form the change map representing the intensity of change for each location. Furthermore, the method can be extended to multispectral images by a simple concatenation of the extracted binary descriptors for all channels.
The rest of this letter is organized as follows. Sections II and III present the proposed method for unsupervised change detection. The results obtained for synthetic and real data are reported in Section IV, while the conclusions are summarized in the last section.