scholarly journals Spectral Optimization of Airborne Multispectral Camera for Land Cover Classification: Automatic Feature Selection and Spectral Band Clustering

2020 ◽  
Author(s):  
Arnaud Le Bris ◽  
Nesrine Chehata ◽  
Xavier Briottet ◽  
Nicolas Paparoditis
Keyword(s):  
Feature Selection ◽  
Land Cover ◽  
Spectral Band ◽  
Land Cover Classification ◽  
Hyperspectral Data ◽  
Data Sets ◽  
Relevance Score ◽  
Spectral Bands ◽  
Optimization Heuristics ◽  
Spectral Optimization

Hyperspectral imagery consists of hundreds of contiguous spectral bands. However, most of them are redundant. Thus a subset of well-chosen bands is generally sufficient for a specific problem, enabling to design adapted superspectral sensors dedicated to specific land cover classification. Related both to feature selection and extraction, spectral optimization identifies the most relevant band subset for specific applications, involving a band subset relevance score as well as a method to optimize it. This study first focuses on the choice of such relevance score. Several criteria are compared through both quantitative and qualitative analyses. To have a fair comparison, all tested criteria are compared to classic hyperspectral data sets using the same optimization heuristics: an incremental one to assess the impact of the number of selected bands and a stochastic one to obtain several possible good band subsets and to derive band importance measures out of intermediate good band subsets. Last, a specific approach is proposed to cope with the optimization of bandwidth. It consists in building a hierarchy of groups of adjacent bands, according to a score to decide which adjacent bands must be merged, before band selection is performed at the different levels of this hierarchy.

scholarly journals EXTRACTION OF OPTIMAL SPECTRAL BANDS USING HIERARCHICAL BAND MERGING OUT OF HYPERSPECTRAL DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 459-465 ◽  
Author(s):  
A. Le Bris ◽  
N. Chehata ◽  
X. Briottet ◽  
N. Paparoditis
Keyword(s):  
Feature Selection ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Data Set ◽  
Spectral Bands ◽  
Relevance Criterion ◽  
Spectral Optimization ◽  
The One ◽  
Selection Algorithms ◽  
Different Levels

Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms. On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Two approaches were proposed to achieve this task : a greedy one and a new adaptation of an incremental feature selection algorithm to this hierarchy of merged bands.

Combining Landsat-8 spectral bands with ancillary variables for land cover classification in mountainous terrains of northern Pakistan

2021 ◽  
Vol 18 (9) ◽  
pp. 2388-2401
Author(s):  
Arif Ur Rehman ◽  
Sami Ullah ◽  
Muhammad Shafique ◽  
Muhammad Sadiq Khan ◽  
Muhammad Tariq Badshah ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Landsat 8 ◽  
Northern Pakistan ◽  
Spectral Bands

scholarly journals Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3798
Author(s):  
Lei Ma ◽  
Michael Schmitt ◽  
Xiaoxiang Zhu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Series Data ◽  
Spectral Bands ◽  
Object Based ◽  
Spatio Temporal ◽  
Optical Satellite Images ◽  
The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

scholarly journals Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

2015 ◽  
Vol 8 (1) ◽  
pp. 3 ◽  
Author(s):  
Shezhou Luo ◽  
Cheng Wang ◽  
Xiaohuan Xi ◽  
Hongcheng Zeng ◽  
Dong Li ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Hyperspectral Data

scholarly journals IMPROVING CLASSIFICATION OF MULTISPECTRAL IMAGES BASED ON SELECTED RELATIONS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 377-381
Author(s):  
L. Cohen ◽  
O. Almog ◽  
M. Shoshany
Keyword(s):  
Land Cover ◽  
Spectral Band ◽  
Research Area ◽  
Support Vector ◽  
Land Cover Types ◽  
Spectral Bands ◽  
Definition Of ◽  
Spectral Representations ◽  
The City

Abstract. A novel classification technique based on definition of unique spectral relations (such as slopes among spectral bands) for all land cover types named (SSF Significant Spectral Features) is presented in the article.A large slopes combination between spectral band pairs is calculated and spectral characterizations that emphasizes the best spectral land cover separation is sought. Increasing in dimensionality of spectral representations is balanced by the simplicity of calculations. The technique has been examined on data acquired by a flown hyperspectral scanner (AISA). The spectral data was narrowed into the equivalent 8 world-view2 channels. The research area was in the city of “Hadera”, Israel, which included 10 land cover types in an urban area, open area and road infrastructure. The comparison between the developed SSF technique and common techniques such as: SVM (Support Vector Machine) and ML (Maximum Likelihood) has shown a clear advantage over ML technique, while produced similar results as SVM. The poorest results of using SSF technique was achieved in an herbaceous area (70%). However, the simplicity of the method, the well-defined parameters it produces for interpreting the results, makes it intuitive over using techniques such as SVM, which is considered as a not explicit classifier.

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