remote-sensing

Exploring how LLMs can enhance remote sensing applications like LULC and image classification using Sentinel, Landsat-8, and THEOS imagery.

In this paper, we present MeViT (Medium-Resolution Vision Transformer), designed for semantic segmentation of Landsat satellite imagery, focusing on key economic crops in Thailand para rubber, corn, and pineapple. MeViT enhances Vision Transformers (ViTs) by integrating medium-resolution multi-branch architectures and revising mixed-scale convolutional feedforward networks (MixCFN) to extract multi-scale local information. Extensive experiments on a public Thailand dataset demonstrate that MeViT outperforms state-of-the-art deep learning methods, achieving a precision of 92.22%, recall of 94.69%, F1 score of 93.44%, and mean IoU of 83.63%. These results highlight MeViT’s effectiveness in accurately segmenting Thai Landsat-8 data.

Flooding poses a significant challenge in Thailand due to its complex geography, traditionally addressed through GIS methods like the Flood Risk Assessment Model (FRAM) combined with the Analytical Hierarchy Process (AHP). This study assesses the efficacy of Artificial Neural Networks (ANN) in flood susceptibility mapping, using data from Ayutthaya Province and incorporating 5-fold cross-validation and Stochastic Gradient Descent (SGD) for training. ANN achieved superior performance with precision of 79.90%, recall of 79.04%, F1-score of 79.08%, and accuracy of 79.31%, outperforming the traditional FRAM approach. Notably, ANN identified that only three factors—flow accumulation, elevation, and soil types—were crucial for predicting flood-prone areas. This highlights the potential for ANN to simplify and enhance flood risk assessments. Moreover, the integration of advanced machine learning techniques underscores the evolving capability of AI in addressing complex environmental challenges.

Semantic segmentation on Landsat-8 data is crucial in the integration of diverse data, allowing researchers to achieve more productivity and lower expenses. This research aimed to improve the versatile backbone for dense prediction without convolutions—namely, using the pyramid vision transformer (PRM-VS-TM) to incorporate attention mechanisms across various feature maps. Furthermore, the PRM-VS-TM constructs an end-to-end object detection system without convolutions and uses handcrafted components, such as dense anchors and non-maximum suspension (NMS). The present study was conducted on a private dataset, i.e., the Thailand Landsat-8 challenge. There are three baselines, DeepLab, Swin Transformer (Swin TF), and PRM-VS-TM. Results indicate that the proposed model significantly outperforms all current baselines on the Thailand Landsat-8 corpus, providing F1-scores greater than 80% in almost all categories. Finally, we demonstrate that our model, without utilizing pre-trained settings or any further post-processing, can outperform current state-of-the-art (SOTA) methods for both agriculture and forest classes.

My PhD thesis focuses on improving semantic segmentation of aerial and satellite images, a crucial task for applications like agriculture planning, map updates, route optimization, and navigation. Current models like the Deep Convolutional Encoder-Decoder (DCED) have limitations in accuracy due to their inability to recover low-level features and the scarcity of training data. To address these issues, I propose a new architecture with five key enhancements, a Global Convolutional Network (GCN) for improved feature extraction, channel attention for selecting discriminative features, domain-specific transfer learning to address data scarcity, Feature Fusion (FF) for capturing low-level details, and Depthwise Atrous Convolution (DA) for refining features. Experiments on Landsat-8 datasets and the ISPRS Vaihingen benchmark showed that my proposed architecture significantly outperforms the baseline models in remote sensing imagery.

This paper addresses improving semantic segmentation in remote sensing for aerial and satellite images, which is crucial for agriculture, map updates, route optimization, and navigation. We propose enhancements to the state-of-the-art Enhanced Global Convolutional Network (GCN152-TL-A) by introducing a High-Resolution Representation (HR) backbone for better feature extraction, Feature Fusion (FF) to capture low-level details, and Depthwise Atrous Convolution (DA) for refined multi-resolution features. Experiments on Landsat-8 and ISPRS Vaihingen datasets demonstrate our model’s superior performance, achieving over 90% accuracy in F1 scores and outperforming baseline models.

In the remote sensing domain, it is crucial to complete semantic segmentation on the raster images, e.g., river, building, forest, etc., on raster images. A deep convolutional encoder–decoder (DCED) network is the state-of-the-art semantic segmentation method for remotely sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose applying a recent CNN called a global convolutional network (GCN), since it can capture different resolutions by extracting multi-scale features from different stages of the network. Additionally, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, “channel attention” is presented in our network in order to select the most discriminative filters (features). Third, “domain-specific transfer learning” is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given datasets (i) medium resolution data collected from Landsat-8 satellite and (ii) very high resolution data called the ISPRS Vaihingen Challenge Dataset. The results show that our networks outperformed DCED in terms of 𝐹1 for 17.48% and 2.49% on medium and very high resolution corpora, respectively.

Semantic Segmentation is a fundamental task in computer vision and remote sensing imagery. Many applications, such as urban planning, change detection, and environmental monitoring, require the accurate segmentation; hence, most segmentation tasks are performed by humans. Currently, with the growth of Deep Convolutional Neural Network (DCNN), there are many works aiming to find the best network architecture fitting for this task. However, all of the studies are based on very-high resolution satellite images, and surprisingly; none of them are implemented on medium resolution satellite images. Moreover, no research has applied geoinformatics knowledge. Therefore, we purpose to compare the semantic segmentation models, which are FCN, SegNet, and GSN using medium resolution images from Landsat-8 satellite. In addition, we propose a modified SegNet model that can be used with remote sensing derived indices. The results show that the model that achieves the highest accuracy RGB bands of medium resolution aerial imagery is SegNet. The overall accuracy of the model increases when includes Near Infrared (NIR) and Short-Wave Infrared (SWIR) band. The results showed that our proposed method (our modified SegNet model, named RGB-IR-IDX-MSN method) outperforms all of the baselines in terms of mean F1 scores.

Semantic segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images has numerous application domains, particularly in road extraction, where the segmented objects serve as essential layers in geospatial databases. Despite several efforts to use deep convolutional neural networks (DCNNs) for road extraction from remote sensing images, accuracy remains a challenge. This paper introduces an enhanced DCNN framework specifically designed for road extraction from remote sensing images by incorporating landscape metrics (LMs) and conditional random fields (CRFs). Our framework employs the exponential linear unit (ELU) activation function to improve the DCNN, leading to a higher quantity and more accurate road extraction. Additionally, to minimize false classifications of road objects, we propose a solution based on the integration of LMs. To further refine the extracted roads, a CRF method is incorporated into our framework. Experiments conducted on Massachusetts road aerial imagery and Thailand Earth Observation System (THEOS) satellite imagery datasets demonstrated that our proposed framework outperforms SegNet, a state-of-the-art object segmentation technique, in most cases regarding precision, recall, and F1 score across various types of remote sensing imagery.

In this paper, we introduce an improved deep convolutional encoder-decoder network (DCED) for segmenting road objects from aerial images. Enhancements include the use of ELU (exponential linear unit) instead of ReLU, dataset augmentation with incrementally-rotated images to increase training data by eight times, and the use of landscape metrics to remove false road objects. Tested on the Massachusetts Roads dataset, our method outperformed the SegNet benchmark and other baselines in precision, recall, and F1 scores.