One-dimensional compressive sensing measurement data based on Gaussian measurement matrix not only well retain sparse signal’s energy, but also inherited sparse signal’s direction information. However in the one-dimensional compression sensing model, direction information can not be applied to sparse signal reconstruction and examination. Two-dimensional compressive sensing model was proposed based on sparse features of change area in the remote sensing image. By use of energy and direction information, sparse signal reconstruction algorithm (2DOMP) was constructed based on two-dimensional compressed sensing. Theoretical analysis and experimental results demonstrated that signal reconstruction ability of 2DOMP algorithm is stronger than other methods. Meanwhile, the concepts of directional remote sensing and directional change are put forward based on the fact that very little measurement data are required to recovery sparse signal by compressive sensing.