1. Amin Zehtabian, Hassan Ghassemian CONCLUSION & FUTURE WORKS
A FULLY ADAPTIVE OBJECT EXTRACTION TECHNIQUE USED FOR SPECTRAL-SPATIAL CLASSIFICATION OF REMOTELY SENSED DATA
Amin Zehtabian, Hassan Ghassemian
  Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
ABSTRACT
Object-based image classification has been frequently addressed in literature, especially for remote sensing applications. Object-based classifiers often benefit from a segmentation step before the classification process in order to generate objects. In this paper, we propose to use the Pixon concept for image segmentation. Meanwhile, in order to form objects which are spectrally homogenous, spatial smoothing is applied as a preprocessing step through using regularized nonlinear partial differential equations (RegAPDE). The parameters of RegAPDE as well as important thresholds used in the Pixon extraction technique are adaptively tuned using four different adaptation algorithms. We also localize the smoothing process via separately applying the RegAPDE algorithm to individual partitions extracted from each layer of the Hyperspectral data using the Watershed transformation.
ADAPTATION METHOD
(one of the four proposed approaches)
𝑑 𝑛𝑠 𝑖 = 𝑑 𝑛(𝑖) − 𝑑 𝑠(𝑖) , …
𝑑 𝑛𝑒.𝑠𝑤 𝑖 = 𝑑 𝑛𝑒(𝑖) − 𝑑 𝑠𝑤 𝑖 , …
𝑣 𝑖 =𝑣𝑎𝑟( 𝑑 𝑖 )
The methodology of adaptively finding proper Pixon extraction thresholds (PETs) for each band of data (there are still three other proposed methods to adaptively find the PETs as well as PDE parameters).
EXPERIMENTAL RESULTS
The gained results for Pavia University and Indian Pines datasets are respectively shown and reported in followings:
Pixel-based
GTM
Object-based
METHODOLOGY
Fig.1. . Briefed block-diagram of the proposed fully-adaptive object-based classification framework
Indian Pines Data
(50 training samples per class)
𝜕𝐼(𝑥,𝑦,𝑡) 𝜕𝑡 =𝛻. (𝑐 𝛻 I 𝜎 𝛻𝐼 𝑥,𝑦,𝑡 )
Fig.2. The merging direction and discipline of the spectral vectors (or pixels) to create Pixons [1]
Regularized nonlinear partial differential equations, used for smoothing the data [2]
Referring to [1], the proposed Pixon extraction algorithm is categorized as a hierarchical clustering technique in which the pixels are merged together regarding their locations, spectral features and a predefined merging order. In this process, the initial pixel (located in upper-left corner of image) is compared to its neighbours according to a clock-wise order. If the difference between intensities of the two adjacent pixels is lower than a Pixon extraction threshold (PET), the pixels join together. When the first two pixels are merged, their averaged intensity is compared to the intensities of the next pixels and the comparisons continue till all the pixels placed in the neighborhood of the initial pixel are analyzed. By this, the first Pixon is created. This process goes on to extract all the Pixons.
CONCLUSION & FUTURE WORKS
Applying complex PDEs instead of traditional PDEs, using Genetic Algorithm for optimization as well as proposing an innovative dist-ance metric lead us to better results (as briefly addressed in the appendix)
REFERENCES
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