The integration of GIS, remote sensing, expert systems and adaptive co-kriging for environmental habitat modeling of the Highland Haggis using object-oriented, fuzzy-logic and neural-network techniques

1996 ◽  
Vol 22 (5) ◽  
pp. 585-588 ◽  
Author(s):  
Oleg McNoleg
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Fuzzy Logic ◽  
Expert Systems ◽  
Object Oriented ◽  
Habitat Modeling

scholarly journals THE EXTRACTION OF POST-EARTHQUAKE BUILDING DAMAGE INFORMATIOM BASED ON CONVOLUTIONAL NEURAL NETWORK

2018 ◽  
Vol XLII-3 ◽  
pp. 161-165 ◽  
Author(s):  
M. Chen ◽  
X. Wang ◽  
A. Dou ◽  
X. Wu
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Yunnan Province ◽  
Contextual Information ◽  
Object Oriented ◽  
Feature Space ◽  
Building Damage ◽  
Aerial Image ◽  
Object Oriented Methods ◽  
Collapsed Building

The seismic damage information of buildings extracted from remote sensing (RS) imagery is meaningful for supporting relief and effective reduction of losses caused by earthquake. Both traditional pixel-based and object-oriented methods have some shortcoming in extracting information of object. Pixel-based method can’t make fully use of contextual information of objects. Object-oriented method faces problem that segmentation of image is not ideal, and the choice of feature space is difficult. In this paper, a new stratage is proposed which combines Convolution Neural Network (CNN) with imagery segmentation to extract building damage information from remote sensing imagery. the key idea of this method includes two steps. First to use CNN to predicate the probability of each pixel and then integrate the probability within each segmentation spot. The method is tested through extracting the collapsed building and uncollapsed building from the aerial image which is acquired in Longtoushan Town after Ms 6.5 Ludian County, Yunnan Province earthquake. The results show that the proposed method indicates its effectiveness in extracting damage information of buildings after earthquake.

Review of Improvement of Web Search Based on Web Log File

2012 ◽  
Vol 3 (2) ◽  
pp. 298-300 ◽  
Author(s):  
Soniya P. Chaudhari ◽  
Prof. Hitesh Gupta ◽  
S. J. Patil
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Web Search ◽  
Pattern Mining ◽  
Efficiency Improvement ◽  
Web Searching ◽  
Important Method ◽  
Web Log ◽  
Journal Paper ◽  
Log File

In this paper we review various research of journal paper as Web Searching efficiency improvement. Some important method based on sequential pattern Mining. Some are based on supervised learning or unsupervised learning. And also used for other method such as Fuzzy logic and neural network

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Digital Image Processing for Detecting and Classifying Plant Diseases

2017 ◽  
Vol 2 (11) ◽  
pp. 1-7
Author(s):  
Izay A. ◽  
Onyejegbu L. N.
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Programming Language ◽  
Design Methodology ◽  
Fuzzy Logic Controller ◽  
Object Oriented ◽  
Plant Diseases ◽  
Analysis And Design ◽  
Crop Diseases ◽  
Growing Population

Agriculture is the backbone of human sustenance in this world. With growing population, there is need for increased productivity in agriculture to be able to meet the demands. Diseases can occur on any part of a plant, but in this paper only the symptoms in the fruits of a plant is considered using segmentation algorithm and edge/ sizing detectors. We also looked at image processing using fuzzy logic controller. The system was designed using object oriented analysis and design methodology. It was implemented using MySQL for the database, and PHP programming language. This system will be of great benefit to farmers and will encourage them in investing their resources since crop diseases can be detected and eliminated early.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

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