Efficient Semantic Segmentation of Hyperspectral Images using Adaptable Rectangular Convolution

Author(s):  
Jose L. Garcia ◽  
Mercedes E. Paoletti ◽  
Luis I. Jimenez ◽  
Juan M. Haut ◽  
Antonio Plaza
Author(s):  
Stojan Trajanovski ◽  
Caifeng Shan ◽  
Pim J.C. Weijtmans ◽  
Susan G. Brouwer de Koning ◽  
Theo J. M. Ruers

2021 ◽  
Vol 13 (17) ◽  
pp. 3497
Author(s):  
Le Sun ◽  
Xiangbo Song ◽  
Huxiang Guo ◽  
Guangrui Zhao ◽  
Jinwei Wang

In order to overcome the disadvantages of convolution neural network (CNN) in the current hyperspectral image (HSI) classification/segmentation methods, such as the inability to recognize the rotation of spatial objects, the difficulty to capture the fine spatial features and the problem that principal component analysis (PCA) ignores some important information when it retains few components, in this paper, an HSI segmentation model based on extended multi-morphological attribute profile (EMAP) features and cubic capsule network (EMAP–Cubic-Caps) was proposed. EMAP features can effectively extract various attributes profile features of entities in HSI, and the cubic capsule neural network can effectively capture complex spatial features with more details. Firstly, EMAP algorithm is introduced to extract the morphological attribute profile features of the principal components extracted by PCA, and the EMAP feature map is used as the input of the network. Then, the spectral and spatial low-layer information of the HSI is extracted by a cubic convolution network, and the high-layer information of HSI is extracted by the capsule module, which consists of an initial capsule layer and a digital capsule layer. Through the experimental comparison on three well-known HSI datasets, the superiority of the proposed algorithm in semantic segmentation is validated.


2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Chenyong Miao ◽  
Alejandro Pages ◽  
Zheng Xu ◽  
Eric Rodene ◽  
Jinliang Yang ◽  
...  

This study describes the evaluation of a range of approaches to semantic segmentation of hyperspectral images of sorghum plants, classifying each pixel as either nonplant or belonging to one of the three organ types (leaf, stalk, panicle). While many current methods for segmentation focus on separating plant pixels from background, organ-specific segmentation makes it feasible to measure a wider range of plant properties. Manually scored training data for a set of hyperspectral images collected from a sorghum association population was used to train and evaluate a set of supervised classification models. Many algorithms show acceptable accuracy for this classification task. Algorithms trained on sorghum data are able to accurately classify maize leaves and stalks, but fail to accurately classify maize reproductive organs which are not directly equivalent to sorghum panicles. Trait measurements extracted from semantic segmentation of sorghum organs can be used to identify both genes known to be controlling variation in a previously measured phenotypes (e.g., panicle size and plant height) as well as identify signals for genes controlling traits not previously quantified in this population (e.g., stalk/leaf ratio). Organ level semantic segmentation provides opportunities to identify genes controlling variation in a wide range of morphological phenotypes in sorghum, maize, and other related grain crops.


2018 ◽  
Vol 11 (6) ◽  
pp. 304
Author(s):  
Javier Pinzon-Arenas ◽  
Robinson Jimenez-Moreno ◽  
Ruben Hernandez-Beleno

2013 ◽  
Vol 11 (1) ◽  
pp. 8-13
Author(s):  
V. Behar ◽  
V. Bogdanova

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6


PIERS Online ◽  
2010 ◽  
Vol 6 (5) ◽  
pp. 480-484 ◽  
Author(s):  
Imed Riadh Farah ◽  
Selim Hemissi ◽  
Karim Saheb Ettabaa ◽  
Bassel Souleiman

2017 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
VEERA NAVENDRA REDDY K ◽  
SIVAPPAGARI CHANDRA MOHAN REDDY ◽  
◽  
Keyword(s):  

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  

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