ISPRS Journal of Photogrammetry and Remote SensingMountrakis, G., Li, J., Lu, X., Hellwich, O., 2018. Deep learning for remotely sensed data. ISPRS J. Photogramm. Remote Sens. 145, 1-2. https://doi.org/10.1016/j.isprsjprs.2018.08.011...
While several methods have been devised to extract and select features, these methods are not themselves learned from the data, and are thus potentially sub-optimal. In recent years, deep learning methods and Convolutional Neural Networks (CNNs) in particular (LeCun et al., 1989) have ...
Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neu...
Mixed data learning Few & zero shot learning Self-supervised, unsupervised & contrastive learning Weakly & semi-supervised learning Active learning Federated Learning Transformers Adversarial ML Image registration Terrain mapping, Disparity Estimation, Lidar, DEMs & NeRF Thermal Infrared SAR NDVI-Vegetation ...
The identification and mapping of trees via remotely sensed data for application in forest management is an active area of research. Previously proposed methods using airborne and hyperspectral sensors can identify tree species with high accuracy but are
deep learning techniques have been extensively carried out to identify remote sensed data obtained by the instruments of Earth observation. Hyperspectral imaging (HSI) is an evolving area in the study of remotely sensed data due to the huge volume of information found in these images, which enables...
Recently, machine and deep learning models have emerged as an efficient tool to predict surface soil moisture at high spatial and temporal scales43,44,45. Unlike physical models, machine or deep learning models are data-driven. They combine different relevant input features to map the output. Fo...
In this paper, we formulate the freezing-tolerant material recognition as a classification problem, which can be solved well using deep learning. However, deep learning is a data-driven method, we need to prepare a large number of samples with ground truth to train the model. In this study,...
[72], present a deep learning technique for monitoring agricultural drought in South Asia using remotely sensed data, specifically for predicting SMDI behavior. In this work, a Deep Feedforward Neural Network (DFNN) was used, and the results were compared with two machine learning models: the ...
The MCNN-based classification was tested on two well-known hyperspectral data sets with high spatial resolution. The results indicated that the proposed MCNN is more effective than existing methods for hyperspectral image classification. Learning new spatial features is very important to improve ...