A Subspace-Based Change Detection Method for Hyperspectral Images

Remote sensing change detection has played an important role in many applications. Most traditional change detection methods deal with single-band or multispectral remote sensing images. Hyperspectral remote sensing images offer more detailed information on spectral changes so as to present promising change detection performance. The challenge is how to take advantage of the spectral information at such a high dimension. In this paper, we propose a subspace-based change detection (SCD) method for hyperspectral images. Instead of dealing with band-wise changes, the proposed method measures spectral changes. SCD regards the observed pixel in the image of Time 2 as target and constructs the background subspace using the corresponding pixel in the image of Time 1, and additional information. In this paper, two types of additional information, i.e., spatial information in the neighborhood of the corresponding pixel in Time 1, and the spectral information of undesired land-cover types, are used to construct the background subspace for special applications. The subspace distance is calculated to determine whether the target is anomalous with respect to the background subspace. The anomalous pixels are considered as changes. Here, orthogonal subspace projection is employed to calculate the subspace distance, which makes full use of the advantage of the abundant spectral information in hyperspectral imagery, and is also easy to apply. The experimental results using Hyperion data and HJ-1A HSI data indicate that SCD gives more accurate detection results, with a lower false alarm rate, compared with other state-of-the-art methods. SCD with additional information also gives satisfactory results in the experiments, reducing the false alarms caused by misregistration and suppressing the change of undesired land-cover types.