scholarly journals TASK-DEPENDENT BAND-SELECTION OF HYPERSPECTRAL IMAGES BY PROJECTION-BASED RANDOM FORESTS

2016 ◽  
Vol III-7 ◽  
pp. 263-270
Author(s):  
R. Hänsch ◽  
O. Hellwich
Keyword(s):  
Land Cover ◽  
Random Forests ◽  
Classification Performance ◽  
Hyperspectral Images ◽  
Band Selection ◽  
Spectral Correlation ◽  
Spectral Bands ◽  
The Given ◽  
Selection Of

The automatic classification of land cover types from hyperspectral images is a challenging problem due to (among others) the large amount of spectral bands and their high spatial and spectral correlation. The extraction of meaningful features, that enables a subsequent classifier to distinguish between different land cover classes, is often limited to a subset of all available data dimensions which is found by band selection techniques or other methods of dimensionality reduction. This work applies Projection-Based Random Forests to hyperspectral images, which not only overcome the need of an explicit feature extraction, but also provide mechanisms to automatically select spectral bands that contain original (i.e. non-redundant) as well as highly meaningful information for the given classification task. The proposed method is applied to four challenging hyperspectral datasets and it is shown that the effective number of spectral bands can be considerably limited without loosing too much of classification performance, e.g. a loss of 1 % accuracy if roughly 13 % of all available bands are used.

scholarly journals TASK-DEPENDENT BAND-SELECTION OF HYPERSPECTRAL IMAGES BY PROJECTION-BASED RANDOM FORESTS

2016 ◽  
Vol III-7 ◽  
pp. 263-270
Author(s):  
R. Hänsch ◽  
O. Hellwich
Keyword(s):  
Land Cover ◽  
Random Forests ◽  
Classification Performance ◽  
Hyperspectral Images ◽  
Band Selection ◽  
Spectral Correlation ◽  
Spectral Bands ◽  
The Given ◽  
Selection Of

The automatic classification of land cover types from hyperspectral images is a challenging problem due to (among others) the large amount of spectral bands and their high spatial and spectral correlation. The extraction of meaningful features, that enables a subsequent classifier to distinguish between different land cover classes, is often limited to a subset of all available data dimensions which is found by band selection techniques or other methods of dimensionality reduction. This work applies Projection-Based Random Forests to hyperspectral images, which not only overcome the need of an explicit feature extraction, but also provide mechanisms to automatically select spectral bands that contain original (i.e. non-redundant) as well as highly meaningful information for the given classification task. The proposed method is applied to four challenging hyperspectral datasets and it is shown that the effective number of spectral bands can be considerably limited without loosing too much of classification performance, e.g. a loss of 1 % accuracy if roughly 13 % of all available bands are used.

scholarly journals Classification of Hyperspectral Images with CNN in Agricultural Lands

2021 ◽  
Vol 3 (1) ◽  
pp. 6
Author(s):  
Eren Can Seyrek ◽  
Murat Uysal
Keyword(s):  
Classification Performance ◽  
Hyperspectral Images ◽  
Superior Performance ◽  
Support Vector ◽  
World Population ◽  
Agricultural Lands ◽  
Leading Role ◽  
Spectral Bands ◽  
Land Cover Maps

Hyperspectral images (HSI) offer detailed spectral reflectance information about sensed objects through provision of information on hundreds of narrow spectral bands. HSI have a leading role in a broad range of applications, such as in forestry, agriculture, geology, and environmental sciences. The monitoring and management of agricultural lands is of great importance for meeting the nutritional and other needs of a rapidly and continuously increasing world population. In relation to this, classification of HSI is an effective way for creating land use and land cover maps quickly and accurately. In recent years, classification of HSI using convolutional neural networks (CNN), which is a sub-field of deep learning, has become a very popular research topic and several CNN architectures have been developed by researchers. The aim of this study was to investigate the classification performance of CNN model on agricultural HSI scenes. For this purpose, a 3D-2D CNN framework and a well-known support vector machine (SVM) model were compared using the Indian Pines and Salinas Scene datasets that contain crop and mixed vegetation classes. As a result of this study, it was confirmed that use of 3D-2D CNN offers superior performance for classifying agricultural HSI datasets.

Evaluating the Potential of Clonal Selection Optimization Algorithm to Hyperspectral Image Feature Selection

2012 ◽  
Vol 500 ◽  
pp. 799-805 ◽  
Author(s):  
Farhad Samadzadegan ◽  
Shahin Rahmatollahi Namin ◽  
Mohammad Ali Rajabi
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Hyperspectral Image ◽  
Clonal Selection ◽  
Fitness Function ◽  
Hyperspectral Images ◽  
Image Feature ◽  
Spectral Bands ◽  
Selection Algorithms ◽  
Selection Of

The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.

Sign in / Sign up

Export Citation Format

Share Document