Haralick Feature Guided Network for the Improvement of Generalization in Landcover Classification

2019 ◽  
pp. 52-64
Author(s):  
Yuzhun Lin ◽  
Daoji Li ◽  
Chuan Zhao ◽  
Junfeng Xu ◽  
Baoming Zhang
Keyword(s):  
Landcover Classification

Improved Landcover Classification using Online Spectral Data Hallucination

2021 ◽  
Vol 439 ◽  
pp. 316-326
Author(s):  
Saurabh Kumar ◽  
Biplab Banerjee ◽  
Subhasis Chaudhuri
Keyword(s):  
Spectral Data ◽  
Landcover Classification

Landcover classification of satellite images based on an adaptive interval fuzzy c-means algorithm coupled with spatial information

2019 ◽  
Vol 41 (6) ◽  
pp. 2189-2208
Author(s):  
Jindong Xu ◽  
Guozheng Feng ◽  
Baode Fan ◽  
Weiqing Yan ◽  
Tianyu Zhao ◽  
...  
Keyword(s):  
Satellite Images ◽  
Spatial Information ◽  
Fuzzy C Means ◽  
Landcover Classification ◽  
Fuzzy C Means Algorithm

scholarly journals Field-based landcover classification using TerraSAR-X texture analysis

2011 ◽  
Vol 48 (5) ◽  
pp. 799-805 ◽  
Author(s):  
Ali Mahmoud ◽  
Samy Elbialy ◽  
Biswajeet Pradhan ◽  
Manfred Buchroithner
Keyword(s):  
Texture Analysis ◽  
Landcover Classification

Cross-evaluation of measurements of peatland methane emissions on microform and ecosystem scales using high-resolution landcover classification and source weight modelling

2011 ◽  
Vol 151 (7) ◽  
pp. 864-874 ◽  
Author(s):  
Inke Forbrich ◽  
Lars Kutzbach ◽  
Christian Wille ◽  
Thomas Becker ◽  
Jiabing Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Methane Emissions ◽  
Landcover Classification

scholarly journals Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands

2009 ◽  
Vol 6 (1) ◽  
pp. 151-205 ◽  
Author(s):  
F. Bretar ◽  
A. Chauve ◽  
J.-S. Bailly ◽  
C. Mallet ◽  
A. Jacome
Keyword(s):  
Remote Sensing Data ◽  
Field Measurements ◽  
Support Vector ◽  
Lidar Data ◽  
Active Sensors ◽  
Natural Hazard Mitigation ◽  
Full Waveform ◽  
Landcover Classification ◽  
Waveform Lidar ◽  
Full Waveform Lidar

Abstract. This article presents the use of new remote sensing data acquired from airborne full-waveform lidar systems. They are active sensors which record altimeter profiles. This paper introduces a set of methodologies for processing these data. These techniques are then applied to a particular landscape, the badlands, but the methodologies are designed to be applied to any other landscape. Indeed, the knowledge of an accurate topography and a landcover classification is a prior knowledge for any hydrological and erosion model. Badlands tend to be the most significant areas of erosion in the world with the highest erosion rate values. Monitoring and predicting erosion within badland mountainous catchments is highly strategic due to the arising downstream consequences and the need for natural hazard mitigation engineering. Additionaly, beyond the altimeter information, full-waveform lidar data are processed to extract intensity and width of echoes. They are related to the target reflectance and geometry. Wa will investigate the relevancy of using lidar-derived Digital Terrain Models (DTMs) and to investigate the potentiality of the intensity and width information for 3-D landcover classification. Considering the novelty and the complexity of such data, they are presented in details as well as guidelines to process them. DTMs are then validated with field measurements. The morphological validation of DTMs is then performed via the computation of hydrological indexes and photo-interpretation. Finally, a 3-D landcover classification is performed using a Support Vector Machine classifier. The introduction of an ortho-rectified optical image in the classification process as well as full-waveform lidar data for hydrological purposes is then discussed.

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