Classification of hyperspectral images based on weighted DMPs

Date

2012-07-27

Author

Ulusoy, İlkay
MURA, Mauro Dalla

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

185
views

0
downloads

This paper presents a classification method for hyperspectral images utilizing Differential Morphological Profiles (DMPs) which permit to include in the analysis spatial information since they can provide an estimate of the size and contrast characteristics of the structures in an image. Due to the wide variety of objects present in a scene, the pixels belonging to the same semantic structure may not have homogeneous spatial and spectral features. In addition, instead of a single peak (which can be related to a measure of the scale), multiple local maxima and multiple responses are usually observed in the DMP. In order to handle such intra-class variability, class-specific weighting functions are employed in order to differently modulate the DMP values according to the different characteristics of the land cover types. In such way, it is possible to differentiate the behaviors of the DMP for each pixel in the image according to its semantic, providing an increase of the separability of the classes. At first, a DMP computed with opening by reconstruction (DMPO) and one with closing by reconstruction (DMPC) are derived on each of the first principle components extracted from the hyperspectral image. Then, both profiles are weighted by each class-specific weighting function and concatenated in a single data structure. The constructed feature vectors are considered by a random forest classifier.

Subject Keywords

Hyperspectral images, Differential Morphological Profiles, Classification

URI

https://hdl.handle.net/11511/42581

DOI

https://doi.org/10.1109/igarss.2012.6351697

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Alignment of uncalibrated images for multi-view classification Arık, Sercan Ömer; Vural, Elif; Frossard, Pascal (2011-12-29) Efficient solutions for the classification of multi-view images can be built on graph-based algorithms when little information is known about the scene or cameras. Such methods typically require a pairwise similarity measure between images, where a common choice is the Euclidean distance. However, the accuracy of the Euclidean distance as a similarity measure is restricted to cases where images are captured from nearby viewpoints. In settings with large transformations and viewpoint changes, alignment of im...
Developing an integrated system for semi-automated segmentation of remotely sensed imagery Kök, Emre Hamit; Türker, Mustafa; Department of Geodetic and Geographical Information Technologies (2005) Classification of the agricultural fields using remote sensing images is one of the most popular methods used for crop mapping. Most recent classification techniques are based on per-field approach that works as assigning a crop label for each field. Commonly, the spatial vector data is used for the boundaries of the fields for applying the classification within them. However, crop variation within the fields is a very common problem. In this case, the existing field boundaries may be insufficient for perfo...
Change detection in aerial images Borchani, M; Cloppet, F; Atalay, Mehmet Volkan; Stamon, G (2004-01-01) This paper deals with how to characterize texture and how to get a good description of images with a minimal number of parameters. This procedure is more objective than textual data. Texture characterization has been used in a matching system to detect changes in couples of aerial images taken at two different times using different order of statistics to describe images. The results are quite encouraging.
Integration of environmental variables with satellite images in regional scale vegetation classification Domaç, Ayşegül; Süzen, Mehmet Lütfi; Bilgin, Cemal Can (Informa UK Limited, 2006-04-01) The difficulty of collecting information at conventional field studies and relatively coarse spatial and spectral resolution of Landsat images forced the use of environmental variables as ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos Mountains region of southern central Turkey. In the first part of the study, ordinary vegetation classification is attained by using a maximu...
TRANSFORMATION-INVARIANT DICTIONARY LEARNING FOR CLASSIFICATION WITH 1-SPARSE REPRESENTATIONS Yuzuguler, Ahmet Caner; Vural, Elif; Frossard, Pascal (2014-05-09) Sparse representations of images in well-designed dictionaries can be used for effective classification. Meanwhile, training data available in most realistic settings are likely to be exposed to geometric transformations, which poses a challenge for the design of good dictionaries. In this work, we study the problem of learning class-representative dictionaries from geometrically transformed image sets. In order to efficiently take account of arbitrary geometric transformations in the learning, we adopt a r...

Citation Formats

İ. Ulusoy and M. D. MURA, “Classification of hyperspectral images based on weighted DMPs,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42581.