Classification of industrial disposal illegal dumping site images by using spatial and spectral information together

2003 ◽  
Author(s):  
J.B. Salleh ◽  
M. Tsudagawa
Keyword(s):  
Spectral Information ◽  
Dumping Site ◽  
Illegal Dumping

scholarly journals Isolation and Identification of Mycobacteria from Soils at an Illegal Dumping Site and Landfills in Japan

2006 ◽  
Vol 50 (7) ◽  
pp. 513-524 ◽  
Author(s):  
Yan Wang ◽  
Midori Ogawa ◽  
Kazumasa Fukuda ◽  
Hiroshi Miyamoto ◽  
Hatsumi Taniguchi
Keyword(s):  
Isolation And Identification ◽  
Dumping Site ◽  
Illegal Dumping

scholarly journals Classification of Landsat-8 Imagery Based On Pca And Ndvi Methods

2019 ◽  
Vol 8 (10) ◽  
pp. 4321-4325
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Region Of Interest ◽  
The Other ◽  
Spectral Information ◽  
Landsat 8 ◽  
Statistical Parameters ◽  
Sensing Applications

Remote sensing is an important issue in satellite image classification. In developing a significant sustainable system in agriculture farming, the major concern for remote sensing applications is the crop classification mechanism. The other important application in remote sensing is urban classification which gives the information about houses, roads, buildings, vegetation etc. A superior indicator for the presence of vegetation can be computed from the vegetation indices of a satellite image. This indicator supports in describing the health of vegetation through the image attributes like greenness and density. The other parameter in detecting objects or region of interest is an image is the texture. A satellite image contains spectral information and can be represented by more spectral bands and classification is very tough task. Generally, Classification of individual pixels in satellite images is based on the spectral information. In this research paper Principle component analysis and combination of PCA and NDVI classification methods are applied on Landsat-8 images. These images are acquired from USGS. The performance of these methods is compared in statistical parameters such as Kappa coefficient, overall accuracy, user’s accuracy, precision accuracy and F1 accuracy. In this work existing method is PCA and proposed method is PCA+NDVI. Experimental results shows that the proposed method has better statistical values compared to existing method.

scholarly journals Attention-Based Pyramid Network for Segmentation and Classification of High-Resolution and Hyperspectral Remote Sensing Images

2020 ◽  
Vol 12 (21) ◽  
pp. 3501
Author(s):  
Qingsong Xu ◽  
Xin Yuan ◽  
Chaojun Ouyang ◽  
Yue Zeng
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Large Scale ◽  
Feature Fusion ◽  
Spectral Information ◽  
Spatial Problem ◽  
Remote Sensing Images ◽  
Heavy Weight

Unlike conventional natural (RGB) images, the inherent large scale and complex structures of remote sensing images pose major challenges such as spatial object distribution diversity and spectral information extraction when existing models are directly applied for image classification. In this study, we develop an attention-based pyramid network for segmentation and classification of remote sensing datasets. Attention mechanisms are used to develop the following modules: (i) a novel and robust attention-based multi-scale fusion method effectively fuses useful spatial or spectral information at different and same scales; (ii) a region pyramid attention mechanism using region-based attention addresses the target geometric size diversity in large-scale remote sensing images; and (iii) cross-scale attention in our adaptive atrous spatial pyramid pooling network adapts to varied contents in a feature-embedded space. Different forms of feature fusion pyramid frameworks are established by combining these attention-based modules. First, a novel segmentation framework, called the heavy-weight spatial feature fusion pyramid network (FFPNet), is proposed to address the spatial problem of high-resolution remote sensing images. Second, an end-to-end spatial-spectral FFPNet is presented for classifying hyperspectral images. Experiments conducted on ISPRS Vaihingen and ISPRS Potsdam high-resolution datasets demonstrate the competitive segmentation accuracy achieved by the proposed heavy-weight spatial FFPNet. Furthermore, experiments on the Indian Pines and the University of Pavia hyperspectral datasets indicate that the proposed spatial-spectral FFPNet outperforms the current state-of-the-art methods in hyperspectral image classification.

Classification of orbital objects by spectral information

2020 ◽  
Vol 12 (3) ◽  
pp. 123-140
Author(s):  
A.P. Treshchalin ◽  
A.A. Shemenev ◽  
A.V. Rodin ◽  
D.V. Churbanov
Keyword(s):  
Spectral Information

scholarly journals Target Classification of Similar Spatial Characteristics in Complex Urban Areas by Using Multispectral LiDAR

2022 ◽  
Vol 14 (1) ◽  
pp. 238
Author(s):  
Binhan Luo ◽  
Jian Yang ◽  
Shalei Song ◽  
Shuo Shi ◽  
Wei Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Point Cloud ◽  
Single Channel ◽  
Urban Land ◽  
Spectral Information ◽  
Surface Model ◽  
Three Dimension ◽  
Channel Information

With the rapid modernization, many remote-sensing sensors were developed for classifying urban land and environmental monitoring. Multispectral LiDAR, which serves as a new technology, has exhibited potential in remote-sensing monitoring due to the synchronous acquisition of three-dimension point cloud and spectral information. This study confirmed the potential of multispectral LiDAR for complex urban land cover classification through three comparative methods. Firstly, the Optech Titan LiDAR point cloud was pre-processed and ground filtered. Then, three methods were analyzed: (1) Channel 1, based on Titan data to simulate the classification of a single-band LiDAR; (2) three-channel information and the digital surface model (DSM); and (3) three-channel information and DSM combined with the calculated three normalized difference vegetation indices (NDVIs) for urban land classification. A decision tree was subsequently used in classification based on the combination of intensity information, elevation information, and spectral information. The overall classification accuracies of the point cloud using the single-channel classification and the multispectral LiDAR were 64.66% and 93.82%, respectively. The results show that multispectral LiDAR has excellent potential for classifying land use in complex urban areas due to the availability of spectral information and that the addition of elevation information to the classification process could boost classification accuracy.

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