Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects
Land-use and land-cover change (LULCC) are of importance in natural resource
management, environmental modelling and assessment, and agricultural production …
management, environmental modelling and assessment, and agricultural production …
Remote sensing for map** natural habitats and their conservation status–New opportunities and challenges
Safeguarding the diversity of natural and semi-natural habitats in Europe is one of the aims
set out by the Habitats Directive (Council Directive 92/43/EEC on the conservation of natural …
set out by the Habitats Directive (Council Directive 92/43/EEC on the conservation of natural …
Deep recurrent neural networks for hyperspectral image classification
In recent years, vector-based machine learning algorithms, such as random forests, support
vector machines, and 1-D convolutional neural networks, have shown promising results in …
vector machines, and 1-D convolutional neural networks, have shown promising results in …
Unsupervised spectral–spatial feature learning via deep residual Conv–Deconv network for hyperspectral image classification
Supervised approaches classify input data using a set of representative samples for each
class, known as training samples. The collection of such samples is expensive and time …
class, known as training samples. The collection of such samples is expensive and time …
Land use changes in the coastal zone of China's Hebei Province and the corresponding impacts on habitat quality
X Zhang, W Song, Y Lang, X Feng, Q Yuan, J Wang - Land use policy, 2020 - Elsevier
The coastal zone is a transition zone between land and sea, and has a high biodiversity.
Land use changes in the coastal zone will inevitably have huge impacts on habitat quality …
Land use changes in the coastal zone will inevitably have huge impacts on habitat quality …
Hyperspectral image classification based on 3-D octave convolution with spatial–spectral attention network
In recent years, with the development of deep learning (DL), the hyperspectral image (HSI)
classification methods based on DL have shown superior performance. Although these DL …
classification methods based on DL have shown superior performance. Although these DL …
Automatic design of convolutional neural network for hyperspectral image classification
Hyperspectral image (HSI) classification is a core task in the remote sensing community, and
recently, deep learning-based methods have shown their capability of accurate classification …
recently, deep learning-based methods have shown their capability of accurate classification …
Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network
Deep convolutional neural networks (CNNs) have shown their outstanding performance in
the hyperspectral image (HSI) classification. The success of CNN-based HSI classification …
the hyperspectral image (HSI) classification. The success of CNN-based HSI classification …
Semi-supervised multiscale dynamic graph convolution network for hyperspectral image classification
In recent years, convolutional neural networks (CNNs)-based methods achieve cracking
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …
Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation map** using high spatial resolution GF-1 and SAR data
Vegetation is an integral component of wetland ecosystems. Map** distribution, quality
and quantity of wetland vegetation is important for wetland protection, management and …
and quantity of wetland vegetation is important for wetland protection, management and …