Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective
MD Hossain, D Chen - ISPRS Journal of Photogrammetry and Remote …, 2019 - Elsevier
Image segmentation is a critical and important step in (GEographic) Object-Based Image
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …
Exploring models and data for remote sensing image caption generation
Inspired by recent development of artificial satellite, remote sensing images have attracted
extensive attention. Recently, notable progress has been made in scene classification and …
extensive attention. Recently, notable progress has been made in scene classification and …
Hyperspectral and lidar data applied to the urban land cover machine learning and neural-network-based classification: A review
Rapid technological advances in airborne hyperspectral and lidar systems paved the way
for using machine learning algorithms to map urban environments. Both hyperspectral and …
for using machine learning algorithms to map urban environments. Both hyperspectral and …
SVM-and MRF-based method for accurate classification of hyperspectral images
The high number of spectral bands acquired by hyperspectral sensors increases the
capability to distinguish physical materials and objects, presenting new challenges to image …
capability to distinguish physical materials and objects, presenting new challenges to image …
High-resolution remote sensing image captioning based on structured attention
Automatically generating language descriptions of remote sensing images has become an
emerging research hot spot in the remote sensing field. Attention-based captioning, as a …
emerging research hot spot in the remote sensing field. Attention-based captioning, as a …
Spectral–spatial classification of hyperspectral image based on deep auto-encoder
X Ma, H Wang, J Geng - IEEE Journal of Selected Topics in …, 2016 - ieeexplore.ieee.org
Deep learning, which represents data by a hierarchical network, has proven to be efficient in
computer vision. To investigate the effect of deep features in hyperspectral image (HSI) …
computer vision. To investigate the effect of deep features in hyperspectral image (HSI) …
Segmentation and classification of hyperspectral images using watershed transformation
Hyperspectral imaging, which records a detailed spectrum of light for each pixel, provides an
invaluable source of information regarding the physical nature of the different materials …
invaluable source of information regarding the physical nature of the different materials …
Spectral–spatial classification of hyperspectral imagery based on partitional clustering techniques
Y Tarabalka, JA Benediktsson… - IEEE transactions on …, 2009 - ieeexplore.ieee.org
A new spectral-spatial classification scheme for hyperspectral images is proposed. The
method combines the results of a pixel wise support vector machine classification and the …
method combines the results of a pixel wise support vector machine classification and the …
Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification
In this paper, we propose a novel multiple kernel learning (MKL) framework to incorporate
both spectral and spatial features for hyperspectral image classification, which is called …
both spectral and spatial features for hyperspectral image classification, which is called …
Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers
Y Tarabalka, J Chanussot… - IEEE Transactions on …, 2009 - ieeexplore.ieee.org
A new method for segmentation and classification of hyperspectral images is proposed. The
method is based on the construction of a minimum spanning forest (MSF) from region …
method is based on the construction of a minimum spanning forest (MSF) from region …