Retrieval of land cover information under thin fog in Landsat TM image

2008 ◽  
Author(s):  
Yuchun Wei
Keyword(s):  
Land Cover ◽  
Landsat Tm ◽  
Landsat Tm Image ◽  
Tm Image

scholarly journals Evaluating land cover types from Landsat TM using SAGA GIS for vegetation mapping based on ISODATA and K-means clustering

2021 ◽  
Vol 26 (52) ◽  
pp. 159-165
Author(s):  
Polina Lemenkova
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Landsat Tm ◽  
Land Cover Types ◽  
Environmental Mapping ◽  
Landsat Tm Image ◽  
Tm Image

The paper presents the cartographic processing of the Landsat TM image by the two unsupervised classification methods of SAGA GIS: ISODATA and K-means clustering. The approaches were tested and compared for land cover type mapping. Vegetation areas were detected and separated from other land cover types in the study area of southwestern Iceland. The number of clusters was set to ten classes. The processing of the satellite image by SAGA GIS was achieved using Imagery Classification tools in the Geoprocessing menu of SAGA GIS. Unsupervised classification performed effectively in the unlabeled pixels for the land cover types using machine learning in GIS. Following an iterative approach of clustering, the pixels were grouped in each step of the algorithm and the clusters were reassigned as centroids. The paper contributes to the technical development of the application of machine learning in cartography by demonstrating the effectiveness of SAGA GIS in remote sensing data processing applied for vegetation and environmental mapping.

scholarly journals Classificação de imagens multiespectrais LANDSAT TM e feições de textura: mapeamento da cobertura da terra

FLORESTA ◽  
1997 ◽  
Vol 27 (12) ◽  
Author(s):  
RUTH EMÍLIA NOGUEIRA LOCK ◽  
FLÁVIO FELIPE KIRCHNER
Keyword(s):  
Land Cover ◽  
Texture Analysis ◽  
Maximum Likelihood Method ◽  
Texture Features ◽  
Landsat Tm ◽  
Likelihood Method ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Initial Results

Este trabalho mostra os resultados iniciais de uma pesquisa sobre classificação de imagens multiespectrais considerando feições de textura, aplicada ao mapeamento da cobertura da terra, com ênfase na separação das classes de cobertura vegetal. Para tanto foi efetuado um levantamento bibliográfico e estudo sobre o assunto, que está resumido na parte inicial. Na seqüência relata-se a parte prática, onde foi feita a classificação multiespectral da imagem LANDSAT-5 TM da Ilha de São Francisco do Sul-SC, utilizando o algoritmo de classificação Máxima verossimilhança. Para testar as potencialidades das feições de textura foram efetuadas quatro classificações distintas para obter as mesmas informações agrupadas em dez classes. Na primeira etapa foi efetuada somente a classificação multiespectral, nas outras foram consideradas feições de textura e classificação espectral. Classification of LANDSAT TM’s multiespectral images and texture features: land cover mapping Abstract This paper shows the initial results of a research regarding multiespectral image classification using texture analysis for land cover maping. A bibliographic review was conducted wich is disposed in the first part of this work. Following this, a classification of the LANDSAT TM image of São Francisco island, SC, was performed using the Maximum Likelihood Method. To test the texture analysis potentialities, four distinct classifications were performed to obtain the same informations grouped into ten classes. In the first one only a multiespectral classification was performed, and in the other three the texture analysis was considered.

Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

2008 ◽  
Vol 112 (5) ◽  
pp. 2485-2494 ◽  
Author(s):  
Sirpa Thessler ◽  
Steven Sesnie ◽  
Zayra S. Ramos Bendaña ◽  
Kalle Ruokolainen ◽  
Erkki Tomppo ◽  
...  
Keyword(s):  
Costa Rica ◽  
Rain Forest ◽  
Landsat Tm ◽  
Forest Types ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Discriminant Analyses

Estimation of land surface temperature from atmospherically corrected LANDSAT TM image using 6S and NCEP global reanalysis product

2014 ◽  
Vol 72 (12) ◽  
pp. 5183-5196 ◽  
Author(s):  
Prashant K. Srivastava ◽  
Dawei Han ◽  
Miguel A. Rico-Ramirez ◽  
Michaela Bray ◽  
Tanvir Islam ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landsat Tm ◽  
Reanalysis Product ◽  
Landsat Tm Image ◽  
Tm Image ◽  
Global Reanalysis

scholarly journals Landsat TM image maps of the Shirase and Siple Coast ice streams, West Antarctica

1994 ◽  
Vol 20 ◽  
pp. 407-412 ◽  
Author(s):  
Jane G. Ferrigno ◽  
Jerry L. Mullins ◽  
Jo Anne Stapleton ◽  
Robert A. Bindschadler ◽  
Ted A. Scambos ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Elevation Angle ◽  
Single Band ◽  
Landsat Tm ◽  
Ice Streams ◽  
Cd Rom ◽  
Landsat Tm Image ◽  
West Antarctic ◽  
Tm Image ◽  
Convolution Algorithm

Fifteen 1: 250000 and one 1: 1000 000 scale Landsat Thematic Mapper (TM) image mosaic maps are currently being produced of the West Antarctic ice streams on the Shirase and Siple Coasts. Landsat TM images were acquired between 1984 and 1990 in an area bounded approximately by 78°-82.5°S and 120°- 160° W. Landsat TM bands 2, 3 and 4 were combined to produce a single band, thereby maximizing data content and improving the signal-to-noise ratio. The summed single band was processed with a combination of high- and low-pass filters to remove longitudinal striping and normalize solar elevation-angle effects. The images were mosaicked and transformed to a Lambert conformal conic projection using a cubic-convolution algorithm. The projection transformation was controled with ten weighted geodetic ground-control points and internal image-to-image pass points with annotation of major glaciological features. The image maps are being published in two formats: conventional printed map sheets and on a CD-ROM.

Geomorphologic study of Anhui section of Changjiang River using Landsat TM image

2007 ◽  
Vol 17 (3) ◽  
pp. 250-256 ◽  
Author(s):  
Yueming Qi ◽  
Haiqiao Tan ◽  
Xing Liang
Keyword(s):  
Landsat Tm ◽  
Changjiang River ◽  
Landsat Tm Image ◽  
Tm Image
Sign in / Sign up

Export Citation Format

Share Document