scholarly journals Histology Image Classification Using Supervised Classification and Multimodal Fusion

2010 ◽  
Author(s):  
Tao Meng ◽  
Lin Lin ◽  
Mei-Ling Shyu ◽  
Shu-Ching Chen
Keyword(s):  
Image Classification ◽  
Supervised Classification ◽  
Multimodal Fusion

Hyperspectral Image Classification Based on Artificial Immune System

2012 ◽  
Vol 500 ◽  
pp. 806-812 ◽  
Author(s):  
Farhad Samadzadegan ◽  
Shahin Rahmatollahi Namin ◽  
Mohammad Ali Rajabi
Keyword(s):  
Immune System ◽  
Image Classification ◽  
Supervised Classification ◽  
Hyperspectral Image ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Hyperspectral Image Classification ◽  
Spectral Dimensionality ◽  
Hyperspectral Remote Sensing Images ◽  
Insight Into

The high spectral dimensionality in hyperspectral images causes the reduction of accuracy for common statistical classification methods in these images. Hence the generation and implementation of more complicated methods have gained great importance in this field. One of these methods is the Artificial Immune Systems which is inspired by natural immune system. Despite its great potentiality, it is rarely utilized for spatial sciences and image classification. In this paper a supervised classification algorithm with the application of hyperspectral remote sensing images is proposed. In order to gain better insight into its capability, its accuracy is compared with Artificial Neural Network. The results show better image classification accuracy for the Artificial Immune method.

scholarly journals PERBAIKAN METODE IDENTIFIKASI POTENSI PENGEMBANGAN LAHAN UNTUK TAMBAK AIR PAYAU SISTEM EKSTENSIF LEWAT INTEGRASI LOGIKA SAMAR DAN PENGINDERAAN JAUH

2016 ◽  
Vol 5 (2) ◽  
pp. 317
Author(s):  
Tarunamulia Tarunamulia ◽  
Jesmond Sammut ◽  
Akhmad Mustafa
Keyword(s):  
Fuzzy Logic ◽  
Maximum Likelihood ◽  
Image Classification ◽  
Supervised Classification ◽  
Training Data ◽  
Classification Method ◽  
Classification Methods ◽  
Elevation Data ◽  
Landsat 7 ◽  
Brackishwater Pond

Tersedianya data potensi lahan tambak yang cepat, akurat dan lengkap untuk kebutuhan pengelolaan kawasan pengembangan perikanan budidaya air payau harus didukung oleh metode identifikasi yang efektif dan efisien. Penelitian ini bertujuan untuk mengupayakan peningkatan kualitas metode klasifikasi multispektral dalam penginderaan jauh dalam mengidentifikasi potensi lahan tambak ekstensif dengan mengintegrasikan logika samar dalam proses klasifikasi citra. Citra landsat-7 ETM+ (30 m), data elevasi digital dan data pengecekan lapang untuk wilayah pantai (kawasan tambak ekstensif/tradisional) Kecamatan Kembang Tanjung, Pidie, Nangroe Aceh Darussalam (NAD) digunakan sebagai bahan utama dalam penelitian ini. Klasifikasi multispektral standar secara terbimbing diperbaiki melalui pengambilan data training secara cermat, yang diikuti dengan uji keterpisahan objek, pemrosesan pasca-klasifikasi dan analisis tingkat ketelitian. Hasil klasifikasi dengan tingkat ketelitian terbaik dari berbagai algoritma yang diujikan untuk tiga saluran selanjutnya dibandingkan dengan hasil klasifikasi dengan menggunakan logika samar. Dari hasil penelitian diketahui bahwa klasifikasi multispektral standar dengan algoritma Maximum Likelihood mampu menghasilkan informasi penutup lahan yang cukup lengkap dan rinci pada wilayah pertambakan dengan ketelitian yang cukup baik (>86%). Tingkat ketelitian yang sama juga masih dijumpai walaupun hanya melibatkan kombinasi 3 saluran terbaik (5,4, dan 3) yang dipilih berdasarkan analisis statistik nilai kecerahan piksel. Dengan membandingkan hasil terbaik dari metode klasifikasi standar yang berbasis logika biner (boolean) dengan hasil klasifikasi citra dengan logika samar dalam pengklasifikasian wilayah tambak, diketahui bahwa klasifikasi citra dengan logika samar mampu memperlihatkan hasil klasifikasi yang sangat baik untuk menentukan batas wilayah tambak yang tidak bisa dilakukan secara langsung bahkan oleh metode standar dengan algoritma terbaik. Dan dengan penambahan satu variabel kunci untuk tambak ekstensif seperti elevasi dalam klasifikasi, klasifikasi dengan logika samar dapat digunakan untuk memprediksi potensi pengembangan lahan budidaya tambak ekstensif dan kemungkinan tumpang tindih dengan penggunaan lahan lainnya.The availability of immediate, accurate and complete data on potential pond area as a baseline data for land management of brackishwater aquaculture must be supported by effective and efficient identification methods. The objective of this study was to explore the possibility of improving the quality of multispectral image classification methods in identifying potential areas for extensive brackishwater aquaculture through the integration of fuzzy logic and classification of remotely sensed data. 2002 Landsat-7 Enhanced Thematic Mapper Data (30-m pixels), digital elevation data, and groundtruthing of training data (region of interest/ROI) of Kembang Tanjung coastal areas (Pidie, NAD) were used as the primary data in this study. Standard supervised multispectral classification methods were enhanced by collecting appropriate and unbiased training data, applying separability measures of ROI pairs, employing post-classification analysis, and assessing the accuracy of classification results. Different types of standard supervised classification algorithms were evaluated and a classification output with the highest accuracy was selected to be compared with the result from fuzzy logic classification. The study showed that a supervised classification method based on maximum likelihood analysis produced the best classification output of land use-cover over the coastal region (overall accuracy > 86%). The accuracy remained at the same level although it involved only the best composite of 3 bands (5,4, and 3) determined by a rigorous statistical analysis of brightness values of pixels. It was clear that the fuzzy-based classification method was more effective in identifying potential extensive brackishwater pond areas compared to the best standard image classification based on binary logic (maximum likelihood). Also, by integrating elevation data as another key variable to determine the suitability of land for extensive brackishwater aquaculture, the fuzzy classification can be used to more accurately predict potential area suited for brackishwater aquaculture ponds and any possible overlapping activity with other land uses.

scholarly journals SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification

2020 ◽  
Vol 12 (20) ◽  
pp. 3358
Author(s):  
Vasileios Syrris ◽  
Ondrej Pesek ◽  
Pierre Soille
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Satellite Image ◽  
Data Retrieval ◽  
Remote Sensing Image ◽  
Training Data ◽  
Data Sets ◽  
Remote Sensing Image Classification

Automatic supervised classification with complex modelling such as deep neural networks requires the availability of representative training data sets. While there exists a plethora of data sets that can be used for this purpose, they are usually very heterogeneous and not interoperable. In this context, the present work has a twofold objective: (i) to describe procedures of open-source training data management, integration, and data retrieval, and (ii) to demonstrate the practical use of varying source training data for remote sensing image classification. For the former, we propose SatImNet, a collection of open training data, structured and harmonized according to specific rules. For the latter, two modelling approaches based on convolutional neural networks have been designed and configured to deal with satellite image classification and segmentation.

Automated image classification of outlet glaciers in Greenland using deep learning

2020 ◽  
Author(s):  
Melanie Marochov ◽  
Patrice Carbonneau ◽  
Chris Stokes
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Classification ◽  
Supervised Classification ◽  
Greenland Ice Sheet ◽  
Training Data ◽  
Glacier Change ◽  
Outlet Glacier ◽  
Glacial Environments

<p>In recent decades, a wealth of research has focused on elucidating the key controls on the mass loss of the Greenland Ice Sheet and its response to climate forcing, specifically in relation to the drivers of spatio-temporally variable outlet glacier change. Despite the increasing availability of high-resolution satellite data, the time-consuming nature of the manual methods traditionally used to analyse satellite imagery has resulted in a significant bottleneck in the monitoring of outlet glacier change. Recent advances in deep learning applied to image processing have opened up a new frontier in the area of automated delineation of glacier termini. However, at this stage, there remains a paucity of research on the use of deep learning for image classification of outlet glacier landscapes. In this contribution, we apply a deep learning approach based on transfer learning to automatically classify satellite images of Helheim glacier, the fastest flowing outlet glacier in eastern Greenland. The method uses the well-established VGG16 convolutional neural network (CNN), and is trained on 224x224 pixel tiles derived from Sentinel-2 RGB bands, which have a spatial resolution of 10 metres. Based on features learned from ImageNet and limited training data, our deep learning model can classify glacial environments with >85% accuracy. In future stages of this research, we will use a new method originally developed for fluvial settings, dubbed ‘CNN-Supervised Classification’ (CSC). CSC uses a pre-trained CNN (in this case our VGG16 model) to replace the human operator’s role in traditional supervised classification by automatically producing new label data to train a pixel-level neural network classifier for any new image. This transferable approach to image classification of outlet glacier landscapes permits not only automated terminus delineation, but also facilitates the efficient analysis of numerous processes controlling outlet glacier behaviour, such as fjord geometry, subglacial plumes, and supra-glacial lakes.</p>

scholarly journals A Research Framework for Supervised Image Classification For Tornado Chaos Phenomena

2018 ◽  
Vol 7 (4.15) ◽  
pp. 447 ◽  
Author(s):  
W Wanayumini ◽  
O S Sitompul ◽  
M Zarlis ◽  
Saib Suwilo ◽  
A M H Pardede
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Data Acquisition ◽  
Image Classification ◽  
Discrete Cosine Transform ◽  
Supervised Classification ◽  
Image Data ◽  
New Model ◽  
Best Value ◽  
Supervised Image Classification

Unattended classification is a classification which is the process of forming classes conducted by computers. The classes formed in this classification are highly dependent on data acquisition. In the process, this classification classifies pixels based on similarity or spectral similarity. While the supervised classification is a classification carried out by the analyst's direction. The purpose of this study is to build a new model of image-based classification based on chaos phenomena through remote sensing to detect the beginning of the emergence of tornadoes. This research optimizes the search for the best value from a data collection of samples of chaos phenomena in tornadoes through a new model called Citra which is supervised by Chaos Discrete Cosine Transform Spectral Angel Mapper Classification (SiChDCosTSamC). The resulting model can then be used as remote sensing to detect the appearance of the initial tornado. Tests will be carried out using the Protected Image Welding on models based on chaotic / chaotic phenomena. Testing will be carried out on a collection of sample image data sourced from SIO, NOAA, US data. Navy, NGA, GEBCO U.S. PGA / NASA Google IBCAO Geological Geological Survey / Copernicus.  

scholarly journals Satellite Image Classification for Environmental Analysis using Image Processing

2020 ◽  
Vol 9 (3) ◽  
pp. 1513-1517
Keyword(s):  
Image Processing ◽  
Image Classification ◽  
Environmental Analysis ◽  
Supervised Classification ◽  
Satellite Image ◽  
Loss Of Life ◽  
Traditional Technique ◽  
Satellite Image Classification ◽  
The Future

Conventional supervised classification of satellite pictures utilizes a solitary multi-band picture and incidental ground perceptions to build phantom marks of land spread classes. We contrasted this methodology with three choices that get marks from different pictures and timespans. signature speculation, in this unearthly marks, is gotten from various pictures inside one season, however maybe from various years. signature extension, in this phantom marks, is made with information from pictures obtained during various periods of that year; and mixes of development and speculation. Utilizing the information for India, we evaluated the nature of these various marks to characterize the pictures used to infer the mark, and for use in transient mark expansion, i.e., applying a mark acquired from the information of one or quite a long while to pictures from different years. While applying marks to the pictures they were gotten from, signature development improved exactness comparative with the customary strategy, and inconstancy in precision declined uniquely. Conversely, signature speculation didn't improve grouping. While applying marks to pictures of different years (worldly expansion), the traditional technique, utilizing a mark got from a solitary picture, brought about extremely low characterization precision. Mark's development additionally performed ineffectively yet multi-year signature speculation performed much better and this seems, by all accounts, to be a promising methodology in the transient augmentation of ghastly marks for satellite picture arrangements. This project summarizes the different audits on satellite picture characterization strategies and systems. The summary helps the analysts to choose suitable satellite picture characterization strategies or methods dependent on the requirements. Later on, the results acquired from the proposed technique will be an extraordinary measure for anticipating and examining the effect of floods. It will help salvage groups to address high caution regions first in this way, least or no loss of life will be accomplished. In the future, the technique can be adjusted to be utilized for coastline location, urbanization, deforestation, and seismic tremors.

scholarly journals A Research Framework for Supervised Image Classification For Tornado Chaos Phenomena

2019 ◽  
Author(s):  
Akim Manaor Hara Pardede
Keyword(s):  
Remote Sensing ◽  
Data Collection ◽  
Data Acquisition ◽  
Image Classification ◽  
Discrete Cosine Transform ◽  
Supervised Classification ◽  
Image Data ◽  
New Model ◽  
Best Value ◽  
Supervised Image Classification

Unattended classification is a classification which is the process of forming classes conducted by computers. The classes formed in this classification are highly dependent on data acquisition. In the process, this classification classifies pixels based on similarity or spectral similarity. While the supervised classification is a classification carried out by the analyst's direction. The l purpose of this study is to build a new model of image-based classification based on chaos phenomena through remote sensing to detect the beginning of the emergence of tornadoes. This research optimizes the search for the best value from a data collection of samples of chaos phenomena in tornadoes through a new model called Citra which is supervised by Chaos Discrete Cosine Transform Spectral Angel Mapper Classification (SiChDCosTSamC). The resulting model can then be used as remote sensing to detect the appearance of the initial tornado. Tests will be carried out using the Protected Image Welding on models based on chaotic / chaotic phenomena. Testing will be carried out on a collection of sample image data sourced from SIO, NOAA, US data. Navy, NGA, GEBCO U.S. PGA / NASA Google IBCAO Geological Geological Survey / Copernicus

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