Article Multispectral Image Feature Points (original) (raw)

Sensors, 2012

This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.

Performance of Interest Point Descriptors on Hyperspectral Images

Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016

Interest point descriptors (e.g. Scale Invariant Feature Transform, SIFT or Speeded-Up Robust Features, SURF) are often used both for classic image processing tasks (e.g. mosaic generation) or higher level machine learning tasks (e.g. segmentation or classification). Hyperspectral images are recently gaining popularity as a potent data source for scene analysis, material identification, anomaly detection or process state estimation. The structure of hyperspectral images is much more complex than traditional color or monochrome images, as they comprise of a large number of bands, each corresponding to a narrow range of frequencies. Because of varying image properties across bands, the application of interest point descriptors to them is not straightforward. To the best of our knowledge, there has been, to date, no study of performance of interest point descriptors on hyperspectral images that simultaneously integrate a number of methods and use a dataset with significant geometric transformations. Here, we study four popular methods (SIFT, SURF, BRISK, ORB) applied to complex scene recorded from several viewpoints. We presents experimental results by observing how well the methods estimate the 3D cameras' positions, which we propose as a general performance measure.

Interest Points for Hyperspectral Image Data

IEEE Transactions on Geoscience and Remote Sensing, 2009

Interest points are widely used as point-features for image matching. This paper describes robust and efficient algorithms to extract multiscale interest points in hyperspectral images in which structural information is distributed across several spectral bands. The formulation is based on a Gaussian scale-space representation of the hyperspectral data cube, and the use of a principal components decomposition to combine information efficiently across spectral bands. A spectral distance measure is used to characterize spatial relations between neighboring hyperspectral pixels. In addition, we describe methods for preprocessing a pair of hyperspectral images, clustering the spectral signatures of interest points, and using the resulting data for matching points under simple geometric transformations. The stability of the resulting interest points in time-lapse satellite images was determined to be in the range of 52% to 75% in the testing data set that were acquired from variety of landforms like coastal islands of La Parguera, Chesapeake Bay, the Cuprite Mining District of Nevada, and agricultural field images of Kansas and Oklahoma, and thus, they can be used as a foundation for image matching and related image analysis tasks. Index Terms-Clustering, difference of Gaussian (DoG), hyperspectral image, interest points, key points, principal components analysis (PCA), scale-invariant feature transform (SIFT).

Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images

IEEE Transactions on Image Processing, 2017

Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named Spectral-Spatial Scale Invariant Feature Transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.

Multispectral remote sensing image registration via spatial relationship analysis on sift keypoints

2010

Multi-sensor image registration is a challenging task in remote sensing. Considering the fact that multi-sensor devices capture the images at different times, multi-spectral image registration is necessary for data fusion of the images. Several conventional methods for image registration suffer from poor performance due to their sensitivity to scale and intensity variation. The scale invariant feature transform (SIFT) is widely used for image registration and object recognition to address these problems. However, directly applying SIFT to remote sensing image registration often results in a very large number of feature points or keypoints but a small number of matching points with a high false alarm rate. We argue that this is due to the fact that spatial information is not considered during the SIFT-based matching process. This paper proposes a method to improve SIFT-based matching by taking advantage of neighborhood information. The proposed method generates more correct matching points as the relative structure in different remote sensing images are almost static.

Robust key point descriptor for multi-spectral image matching

Journal of Systems Engineering and Electronics, 2014

Histogram of collinear gradient-enhanced coding (HCGEC), a robust key point descriptor for multi-spectral image matching, is proposed. The HCGEC mainly encodes rough structures within an image and suppresses detailed textural information, which is desirable in multi-spectral image matching. Experiments on two multi-spectral data sets demonstrate that the proposed descriptor can yield significantly better results than some state-ofthe-art descriptors.

Interest point detection for hyperspectral imagery

2009

This paper presents an algorithm for automated extraction of interest points (IPs)in multispectral and hyperspectral images. Interest points are features of the image that capture information from its neighbours and they are distinctive and stable under transformations such as translation and rotation. Interest-point operators for monochromatic images were proposed more than a decade ago and have since been studied extensively. IPs have been applied to diverse problems in computer vision, including image matching, recognition, registration, 3D reconstruction, change detection, and content-based image retrieval. Interest points are helpful in data reduction, and reduce the computational burden of various algorithms (like registration, object detection, 3D reconstruction etc) by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. An interest operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, stable under geometric transformation, and interpretable which are good candidates for interest points. Our approach extends ideas from Lowe's keypoint operator that uses local extrema of Difference of Gaussian (DoG) operator at multiple scales to detect interest point in gray level images. The proposed approach extends Lowe's method by direct conversion of scalar operations such as scale-space generation, and extreme point detection into operations that take the vector nature of the image into consideration. Experimental results with RGB and hyperspectral images which demonstrate the potential of the method for this application and the potential improvements of a fully vectorial approach over band-by-band approaches described in the literature.

Interest point detection for hyperspectral imagery

Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 2009

Improved Registration of Infrared Images Using EOH Descriptor

2019

Feature-based image registration involves overlaying two images of the same area by extracting features, matching and computing geometric transformation. Multimodal image registration is useful in a variety of applications as the unique information contained in diverse images can be combined. Descriptors proposed for multimodal image matching such as edge-oriented histogram (EOH), log-Gabor histogram (LGHD) can address the photometric variation between the visual and infrared images better than conventional image descriptors such as SIFT. However, the invariance of such descriptors to geometric variations such as scale and rotation is poor. To address the geometric variations in addition to photometric variations, the region around the feature point is preprocessed using scale and rotation information of detector before deriving the descriptor. Different datasets are composed of images obtained in visible light and infrared spectra images, and IR images contain variations to compare...

Distinctive Order Based Self-Similarity descriptor for multi-sensor remote sensing image matching

Robust, well-distributed and accurate feature matching in multi-sensor remote sensing image is a difficult task duo to significant geometric and illumination differences. In this paper, a robust and effective image matching approach is presented for multi-sensor remote sensing images. The proposed approach consists of three main steps. In the first step, UR-SIFT (Uniform robust scale invariant feature transform) algorithm is applied for uniform and dense local feature extraction. In the second step, a novel descriptor namely Distinctive Order Based Self Similarity descriptor, DOBSS descriptor, is computed for each extracted feature. Finally, a cross matching process followed by a consistency check in the projective transformation model is performed for feature correspondence and mismatch elimination. The proposed method was successfully applied for matching various multi-sensor satellite images as: ETM+, SPOT 4, SPOT 5, ASTER, IRS, SPOT 6, QuickBird, GeoEye and Worldview sensors, and the results demonstrate its robustness and capability compared to common image matching techniques such as SIFT, PIIFD, GLOH, LIOP and LSS.

Article Multispectral Image Feature Points (original) (raw)

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