Multispectral image feature selection for land mine detection

Clark, Gregory A.; Sengupta, S. K.; Aimonetti, W.D.; Roeske, F.; Donetti, J.G.

doi:10.1109/36.823923

Cited by 24 publications

(19 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, feature reduction is a needed step to recognize targets from non-targets. For similar applications, other research used PCA to extract features [23][24][25][26] or FS [27,28] to select a subset of the original features. We use our new HDGD feature extraction algorithm and compare our methods to other standard methods.…”

Section: Automatic Target Detection (Atr) Systemmentioning

confidence: 99%

Mine and vehicle detection in hyperspectral image data: waveband selection

Casasent

Chen

2003

SPIE Proceedings

View full text Add to dashboard Cite

Hyperspectral (HS) data contains spectral response information that provides detailed descriptions of an object. These new sensor data are useful in automatic target recognition applications. However, such high-dimensional data introduces problems due to the curse of dimensionality, the need to reduce the number of features (λ responses) used to accommodate realistic small training set sizes, and the need to employ discriminatory features and still achieve good generalization (comparable training and test set performance). HS sensors produce high-dimensional data; this is characterized by a training set size (N i ) per class that is less than the number of input features (N F ). A new highdimensional generalized discriminant (HDGD) feature extraction algorithm and a new modified branch and bound (MBB) feature selection algorithm are described and compared to other feature reduction methods for two HS target detection applications (mine and vehicle detection). Both space and spectral parameters are adapted. A new blobcoloring hit-miss transform is introduced.

show abstract

Section: Automatic Target Detection (Atr) Systemmentioning

confidence: 99%

Mine and vehicle detection in hyperspectral image data: waveband selection

Casasent

Chen

2003

SPIE Proceedings

View full text Add to dashboard Cite

show abstract

“…However, the second one is more advanced because it allows implementation of complex features, and thus may improve the detection results. The region-based approach was utilized in several works [10], [14], [4], [3], [6]. To implement this concept, the obtained sensor image must be first processed to select the ROIs.…”

Section: Overview Of the Systemmentioning

confidence: 99%

Detection of Natural Landmarks for Mapping by a Demining Robot

Larionova

Marques

Almeida

2006

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

To enable automated landmine detection a number of practical problems must be overcome. One of them is the mapping of data from landmine detection sensors using a mobile demining robot in an unstructured outdoor environment. The odometry of the robot used for mapping can be improved by using an additional vision system. It is proposed in this paper to utilize natural landmarks which can be found on the ground for this purpose. New simple algorithms for detection and association of natural landmarks are developed and described. Finally, the experimental results of applying these algorithms on a set of images obtained from a camera mounted on the robot are presented. The results show their robustness for different environmental conditions and robot motions.

show abstract

“…The terrain varies, including areas with grass and foliage, cleared areas, and sandy areas. Clark and others [9] stated that the successful operational systems for the airbornestandoff mine detection were very limited for surface mines, and did not yet exist for buried mines. Behboodian and others [5] developed a system which used elastic surface waves and electromagnetic waves for the detection of buried land mines.…”

Section: Introductionmentioning

confidence: 98%

HAMD: a software system for surface and buried mine detections

et al. 2008

View full text Add to dashboard Cite

We present an algorithm suite, Hybrid Airborne Mine Detector (HAMD), developed for the detection of small scatterable surface and buried mines, using multispectral airborne images. This algorithm suite is composed of a number of components designed for specific tasks such as image segmentation based on unsupervised clustering, localized image enhancement, generalized signature extraction and construction, and mine classification and fusion. Since both surface and buried mines in low contrast images are difficult to detect, a new algorithm has been developed to enhance images locally. The signature extraction component extracts different signatures based on surface or buried mines. To extract small surface mine signatures, moment invariance (MI) is used. However, to extract buried mine signatures, thermal variations and spatial distributions are employed. To make the system suitable for different operational environments, a small number of general signatures are constructed and stored in the signature library. Test results based on airborne images have shown that signatures collected can be used to detect mines placed in different environments such as vegetation and sandy areas. For mine classification and false alarm mitigation, statistical hypothesis tests, such as Fisher's Discriminant Ratio (FDR) test and the Kolmogorov-Smirnov (KS) test, are used.

show abstract

Multispectral image feature selection for land mine detection

Cited by 24 publications

References 8 publications

Mine and vehicle detection in hyperspectral image data: waveband selection

Mine and vehicle detection in hyperspectral image data: waveband selection

Detection of Natural Landmarks for Mapping by a Demining Robot

HAMD: a software system for surface and buried mine detections

Contact Info

Product

Resources

About