Land Cover Change Detection at Subpixel Resolution With a Hopfield Neural Network

TLDR: It was found that the proposed HNN with an FSRM method can separate more real changes from noise and produce more accurate LCCD results than the state-of-the-art methods.

Abstract: In this paper, a new subpixel resolution land cover change detection (LCCD) method based on the Hopfield neural network (HNN) is proposed. The new method borrows information from a known fine spatial resolution land cover map (FSRM) representing one date for subpixel mapping (SPM) from a coarse spatial resolution image on another, closer date. It is implemented by using the thematic information in the FSRM to modify the initialization of neuron values in the original HNN. The predicted SPM result was compared to the original FSRM to achieve subpixel resolution LCCD. The proposed method was compared with the original unmodified HNN method as well as six state-of-the-art methods for LCCD. To explore the effect of uncertainty in spectral unmixing, which mainly originates from spectral separability in the input, coarse image, and the point spread function (PSF) of the sensor, a set of synthetic multispectral images with different class separabilities and PSFs was used in experiments. It was found that the proposed LCCD method (i.e., HNN with an FSRM) can separate more real changes from noise and produce more accurate LCCD results than the state-of-the-art methods. The advantage of the proposed method is more evident when the class separability is small and the variance in the PSF is large, that is, the uncertainty in spectral unmixing is large. Furthermore, the utilization of an FSRM can expedite the HNN-based processing required for LCCD. The advantage of the proposed method was also validated by applying to a set of real Landsat-Moderate Resolution Imaging Spectroradiometer (MODIS) images.