# Visible Spectrum and Infra-Red Image Matching: A New Method
## 2 Related Work  
Intensity and textural changes between multisensor images degrade the performance of feature points. Various new and modified versions of SIFT algorithm have been proposed to overcome such changes. Gradient Orientation Modification (GOM)-SIFT, is an extension of SIFT, to makes the SIFT descriptor robust against textural and intensity changes. It is based on modifying image gradients around the SIFT feature points [1] and then uses the modified gradients for descriptor construction. Orientation Restricted (OR)-SIFT is based on a similar idea. It computes SIFT descriptors and combines the elements of the SIFT descriptor in the opposite orientation directions [2] to overcome textural and intensity changes. In the Edge Oriented Histogram (EOH) method, the distribution and orientation of Canny edges are used instead of image gradients for descriptor construction [5]. The EOH descriptor works well on VS-IR images but under performs in the presence of rotational changes between the images. Directional filters are used in Reference [11] to overcome this problem and to construct VAR-EOH descriptors. Similarly, the image edges are computed with Local Contrast (LC) and Differential Excitation (DE) kernels and are used instead of image gradients in the SIFT algorithm to compute LC-SIFT and DE-SIFT descriptors [12]. In the Local Binary Patterns of Gradients (LBPG) [14] approach, the Center Symmetric Local Binary Patterns (CSLBP) [13] is used to modify both gradient magnitude and orientation maps around the feature points. This gives modified image gradients, which are used as features in the construction of LBPG descriptor. LBPG shows superior performance against textural and intensity changes but its large descriptor size (256 dimensions) makes the descriptor matching process computationally expensive. To overcome this limitation, image gradients around the feature points are normalized and used as features in the construction of Normalized Gradient (NG)-SIFT descriptors [15]. NG-SIFT works well on multisensor images of structured scenes, but under performs on textured scene images. In the Modified Normalized gradient (MN)-SIFT method, the MN features are used to overcome this problem [17]. The Local Self Similarity (LSS) descriptor [16] uses self-similarity of pixels, edges, and repetitive patterns in descriptor construction. The extended versions of LSS are Dense Adaptive Self Correlation [19] and Fully Convolutional Self Similarity [20] descriptors, both they are computed in a dense manner over the whole image. [PERSON] et al. [21] propose a phase congruency approach. They generate features by computing amplitude and orientation of the phase congruency model to match multi-modal images. They call the descriptor as Histogram of Orientated Phase Congruency (HOPC), which encapsulates both edges and structural information for multi-modal image matching. [PERSON] et al. [22] propose Local Self Similarity Frequency (LSSF) descriptors. They compute LSSF with features, which are obtained through frequency domain analysis of local internal layout of the self similarities. The LSSF employs a correlation surface to reduce intensity and textural differences between VS and near IR images. LSSF is invariant to rotational changes and uses a log polar binning scheme for descriptor construction. [PERSON] and [PERSON] propose an adaptive binning strategy to construct descriptors for remote sensing images [23]. They use Hessian affine feature point detector to extract normalized image patches for descriptor construction. Unlike SIFT, they use an adaptive histogram quantization strategy to incorporate both location and gradient orientation information to make the descriptors robust against viewpoint, intensity and textural changes. [PERSON] and [PERSON] compute descriptors by extracting structural properties of the image [24]. They use log-Gabor filters and named the descriptor as Multispectral Feature Descriptor (MFD).