Selection of the Informative Feature System for Crops Classification Using Hyperspectral Data

2020 ◽  
Vol 56 (4) ◽  
pp. 431-439
Author(s):  
S. M. Borzov ◽  
O. I. Potaturkin
Keyword(s):  
Hyperspectral Data ◽  
Informative Feature ◽  
Feature System ◽  
Selection Of

A Study on Selecting Segmentation Object of Object-Oriented Inversion Based on Hyperspectral Chlorophyll-A in Chaohu Lake

2014 ◽  
Vol 675-677 ◽  
pp. 1153-1157
Author(s):  
Xue Jiao Hou ◽  
Jing Liu ◽  
Wei Cui
Keyword(s):  
Chlorophyll A ◽  
Object Oriented ◽  
Hyperspectral Data ◽  
Single Band ◽  
Quality Data ◽  
Chaohu Lake ◽  
Band Model ◽  
Water Quality Data ◽  
Combination Model ◽  
Selection Of

Beased on in Situ Water Quality Data and Hyperspectral Data from HJ-1A satellite in Chaohu Lake, through Contrasting the Object-Oriented Chlorophyll-a Inversion Precision Of single Band with Two-Band Model, the Results Show: (1) In Hyperspectral Object-Oriented Remote Sensing inversion, the Inversion Effect of Choosing Combination Model to Segment Is superior to that of Choosing the Single Band Directly, and Using Combination model to Segment can Certain Degree Solve the Problem that Commercial Softwares cannot Segment all Hyperspectral Data at the same Time.(2)When Inversing Chlorophyll-a Concentration with Hyperspectral Data, the Single Bands Constituting the Optimal Model Are not Always in the Traditional Characteristics Band Range of Chlorophyll-a. so All bands should be Comprehensively Analyzed to take Full Advantages Of hyperspectral Data when Inversing. these Conclusions Will provide Basis for the Future Segmentation Object Selection of Object-Orientedon Hyperspectral Lakes Chlorophyll-a Inversion and Certain Reference for Band Selection of Hyperspectral Inversion Model.

scholarly journals Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1020 ◽  
Author(s):  
Bollandsås ◽  
Ørka ◽  
Dalponte ◽  
Gobakken ◽  
Næsset
Keyword(s):  
Atmospheric Correction ◽  
Laser Scanner ◽  
Field Measurements ◽  
Site Index ◽  
Hyperspectral Data ◽  
Average Height ◽  
Single Time ◽  
Development Site ◽  
Corrected Data ◽  
Selection Of

In forest management, site index information is essential for planning silvicultural operations and forecasting forest development. Site index is most commonly expressed as the average height of the dominant trees at a certain index age, and can be determined either by photo interpretation, field measurements, or projection of age combined with height estimates from remote sensing. However, recently it has been shown that site index can be accurately predicted from bi-temporal airborne laser scanner (ALS) data. Furthermore, single-time hyperspectral data have also been shown to be correlated to site index. The aim of the current study was to compare the accuracy of modelling site index using (1) data from bi-temporal ALS; (2) single-time hyperspectral data with different types of preprocessing; and (3) combined bi-temporal ALS and single-time hyperspectral data. The period between the ALS acquisitions was 11 years. The preprocessing of the hyperspectral data included an atmospheric correction and/or a normalization of the reflectance. Furthermore, a selection of pixels was carried out based on NDVI and compared to using all pixels. The results showed that bi-temporal ALS data explained about 70% (R2) of the variation in the site index, and the RMSE values from a cross-validation were 3.0 m and 2.2 m for spruce- and pine-dominated plots, respectively. Corresponding values for the different single-time hyperspectral datasets were 54%, 3.9 m, and 2.5 m. With bi-temporal ALS data and hyperspectral data used in combination, the results indicated that the contribution from the hyperspectral data was marginal compared to just using bi-temporal ALS. We also found that models constructed with normalized hyperspectral data produced lower RMSE values compared to those constructed with atmospherically corrected data, and that a selection of pixels based on NDVI did not improve the results compared to using all pixels.

scholarly journals Analysis of Variable-Length Codes for Integer Encoding in Hyperspectral Data Compression with the k2-Raster Compact Data Structure

2020 ◽  
Vol 12 (12) ◽  
pp. 1983
Author(s):  
Kevin Chow ◽  
Dion Eustathios Olivier Tzamarias ◽  
Miguel Hernández-Cabronero ◽  
Ian Blanes ◽  
Joan Serra-Sagristà
Keyword(s):  
Data Structure ◽  
Compression Ratio ◽  
Hyperspectral Data ◽  
Variable Length ◽  
Direct Access ◽  
Access Time ◽  
Competitive Performance ◽  
K Value ◽  
Data Elements ◽  
Selection Of

This paper examines the various variable-length encoders that provide integer encoding to hyperspectral scene data within a k 2 -raster compact data structure. This compact data structure leads to a compression ratio similar to that produced by some of the classical compression techniques. This compact data structure also provides direct access for query to its data elements without requiring any decompression. The selection of the integer encoder is critical for obtaining a competitive performance considering both the compression ratio and access time. In this research, we show experimental results of different integer encoders such as Rice, Simple9, Simple16, PForDelta codes, and DACs. Further, a method to determine an appropriate k value for building a k 2 -raster compact data structure with competitive performance is discussed.

scholarly journals DW-Net: Dynamic Multi-Hierarchical Weighting Segmentation Network for Joint Segmentation of Retina Layers With Choroid Neovascularization

2021 ◽  
Vol 15 ◽  
Author(s):  
Lianyu Wang ◽  
Meng Wang ◽  
Tingting Wang ◽  
Qingquan Meng ◽  
Yi Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Quantitative Measurement ◽  
State Of The Art ◽  
Complex Structure ◽  
Subsequent Treatment ◽  
Experimental Results ◽  
Informative Feature ◽  
Retinal Layers ◽  
Selection Of

Choroid neovascularization (CNV) is one of the blinding factors. The early detection and quantitative measurement of CNV are crucial for the establishment of subsequent treatment. Recently, many deep learning-based methods have been proposed for CNV segmentation. However, CNV is difficult to be segmented due to the complex structure of the surrounding retina. In this paper, we propose a novel dynamic multi-hierarchical weighting segmentation network (DW-Net) for the simultaneous segmentation of retinal layers and CNV. Specifically, the proposed network is composed of a residual aggregation encoder path for the selection of informative feature, a multi-hierarchical weighting connection for the fusion of detailed information and abstract information, and a dynamic decoder path. Comprehensive experimental results show that our proposed DW-Net achieves better performance than other state-of-the-art methods.

scholarly journals Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

2019 ◽  
Vol 11 (11) ◽  
pp. 1298 ◽  
Author(s):  
Ahmed Laamrani ◽  
Aaron A. Berg ◽  
Paul Voroney ◽  
Hannes Feilhauer ◽  
Line Blackburn ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Least Squares Regression ◽  
Agricultural Field ◽  
Southern Ontario ◽  
Spectral Bands ◽  
Selection Of

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors.

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