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A Performance Comparison between GIS-based and Neuron Network Methods for Flood Susceptibility Assessment in Ayutthaya Province
(2022-01-15)
Vajeethaveesin, Thanat
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Panboonyuen, Teerapong
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Lawawironjwong, Siam
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Srestasathiern, Panu
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Jaiyen, Saichon
Flooding has been a long withstanding issue in Thailand. Due to its geographical setup, mitigation and management of floods are challenging and hard to execute. One of the tools used in managing the events is “flood susceptibility mapping,” in which an incident probability as well as a rescue path is estimated and planned. To create one, the traditional GIS method called FRAM (flood risk assessment model), combined with AHP (analytical hierarchy process), is used and implemented on ArcGIS software. In this method, we first created a comparison table to compute weights for each of the selected factors. Then the computed weights were used in the FRAM model in ArcGIS to create a flood susceptibility map for each region. Each region was then classified as very high, high, medium, low, and very low risk. On the other hand, in computer science, machine learning and AI are prevalent and being adopted to various domains, promising the effectiveness of the method, potentially beat the forementioned traditional method. Therefore, ANN (artificial neural network) is adopted in this work to create the flood susceptibility map. The ANN technique is developed by using causal factors. The ANN classifies areas as either flood areas or flood-free areas. The 2 methods from different disciplines (GIS and Computer Science) are applied and described in this paper with the intention to prove whether the machine learning is really efficient and can outperform the traditional GIS approach. Data on Thailand’ s Ayutthaya Province is used in this work as a case study-in order to assess flood prone areas and compared for performance evaluation. Both of which use the 6 selected factors according to the literature: (i) flow accumulation, (ii) elevation, (iii) land use, (iv) rainfall intensity, (v) slope and (vi) soil types. The results from the 2 methods were verified with historical flood data and compared. The results showed that ANN (obtained via sensitivity analysis) outperformed the FRAM with precision of 79.90 %, recall of 79.04 %, F1-score of 79.08 % and accuracy of 79.31 %. In addition, we found that (according to our ANN experiments) the main causal factors related to flood susceptibility map only included 3 factors: flow accumulation, elevation, and soil types. Therefore, the proposed methodology for assessment of flood susceptibility areas using these 3 factors could be considered sufficient and applied to other regions in related applications, when needed.
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Transformer-based decoder designs for semantic segmentation on remotely sensed images
(2021-12-01)
Panboonyuen, Teerapong
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
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Vateekul, Peerapon
Transformers have demonstrated remarkable accomplishments in several natural language processing (NLP) tasks as well as image processing tasks. Herein, we present a deep-learning (DL) model that is capable of improving the semantic segmentation network in two ways. First, utilizing the pre-training Swin Transformer (SwinTF) under Vision Transformer (ViT) as a backbone, the model weights downstream tasks by joining task layers upon the pretrained encoder. Secondly, decoder designs are applied to our DL network with three decoder designs, U-Net, pyramid scene parsing (PSP) network, and feature pyramid network (FPN), to perform pixel-level segmentation. The results are compared with other image labeling state of the art (SOTA) methods, such as global convolutional network (GCN) and ViT. Extensive experiments show that our Swin Transformer (SwinTF) with decoder designs reached a new state of the art on the Thailand Isan Landsat-8 corpus (89.8% F1 score), Thailand North Landsat-8 corpus (63.12% F1 score), and competitive results on ISPRS Vaihingen. Moreover, both our best-proposed methods (SwinTF-PSP and SwinTF-FPN) even outperformed SwinTF with supervised pre-training ViT on the ImageNet-1K in the Thailand, Landsat-8, and ISPRS Vaihingen corpora.
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Semantic labeling in remote sensing corpora using feature fusion-based enhanced global convolutional network with high-resolution representations and depthwise atrous convolution
(2020-04-01)
Panboonyuen, Teerapong
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
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Vateekul, Peerapon
One of the fundamental tasks in remote sensing is the semantic segmentation on the aerial and satellite images. It plays a vital role in applications, such as agriculture planning, map updates, route optimization, and navigation. The state-of-the-art model is the Enhanced Global Convolutional Network (GCN152-TL-A) from our previous work. It composes two main components: (i) the backbone network to extract features and (ii) the segmentation network to annotate labels. However, the accuracy can be further improved, since the deep learning network is not designed for recovering low-level features (e.g., river, low vegetation). In this paper, we aim to improve the semantic segmentation network in three aspects, designed explicitly for the remotely sensed domain. First, we propose to employ a modern backbone network called "High-Resolution Representation (HR)" to extract features with higher quality. It repeatedly fuses the representations generated by the high-to-low subnetworks with the restoration of the low-resolution representations to the same depth and level. Second, "Feature Fusion (FF)" is added to our network to capture low-level features (e.g., lines, dots, or gradient orientation). It fuses between the features from the backbone and the segmentation models, which helps to prevent the loss of these low-level features. Finally, "Depthwise Atrous Convolution (DA)" is introduced to refine the extracted features by using four multi-resolution layers in collaboration with a dilated convolution strategy. The experiment was conducted on three data sets: two private corpora from Landsat-8 satellite and one public benchmark from the "ISPRS Vaihingen" challenge. There are two baseline models: the Deep Encoder-Decoder Network (DCED) and our previous model. The results show that the proposed model significantly outperforms all baselines. It is the winner in all data sets and exceeds more than 90% of F1: 0.9114, 0.9362, and 0.9111 in two Landsat-8 and ISPRS Vaihingen data sets, respectively. Furthermore, it achieves an accuracy beyond 90% on almost all classes.
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Semantic segmentation on remotely sensed images using an enhanced global convolutional network with channel attention and domain specific transfer learning
(2019-01-01)
Panboonyuen, Teerapong
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
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Vateekul, Peerapon
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.
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Semantic Segmentation on Medium-Resolution Satellite Images Using Deep Convolutional Networks with Remote Sensing Derived Indices
(2018-09-06)
Chantharaj, Sirinthra
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Pornratthanapong, Kissada
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Chitsinpchayakun, Pitchayut
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Panboonyuen, Teerapong
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Vateekul, Peerapon
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.
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An enhanced deep convolutional encoder-decoder network for road segmentation on aerial imagery
(2018-01-01)
Panboonyuen, Teerapong
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Vateekul, Peerapon
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
Object classification from images is among the many practical examples where deep learning algorithms have successfully been applied. In this paper, we present an improved deep convolutional encoder-decoder network (DCED) for segmenting road objects from aerial images. Several aspects of the proposed method are enhanced, incl. incorporation of ELU (exponential linear unit)—as opposed to ReLU (rectified linear unit) that typically outperforms ELU in most object classification cases; amplification of datasets by adding incrementally-rotated images with eight different angles in the training corpus (this eliminates the limitation that the number of training aerial images is usually limited), thus the number of training datasets is increased by eight times; and lastly, adoption of landscape metrics to further improve the overall quality of results by removing false road objects. The most recent DCED approach for object segmentation, namely SegNet, is used as one of the benchmarks in evaluating our method. The experiments were conducted on a well-known aerial imagery, Massachusetts roads dataset (Mass. Roads), which is publicly available. The results showed that our method outperforms all of the baselines in terms of precision, recall, and F1 scores.
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Road segmentation of remotely-sensed images using deep convolutional neural networks with landscape metrics and conditional random fields
(2017-07-01)
Panboonyuen, Teerapong
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
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Srestasathiern, Panu
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Vateekul, Peerapon
Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at applying the deep convolutional neural network (DCNN) to extract roads from remote sensing images have been made; however, the accuracy is still limited. In this paper, we present an enhanced DCNN framework specifically tailored for road extraction of remote sensing images by applying landscape metrics (LMs) and conditional random fields (CRFs). To improve the DCNN, a modern activation function called the exponential linear unit (ELU), is employed in our network, resulting in a higher number of, and yet more accurate, extracted roads. To further reduce falsely classified road objects, a solution based on an adoption of LMs is proposed. Finally, to sharpen the extracted roads, a CRF method is added to our framework. The experiments were conducted on Massachusetts road aerial imagery as well as the Thailand Earth Observation System (THEOS) satellite imagery data sets. The results showed that our proposed framework outperformed Segnet, a state-of-the-art object segmentation technique, on any kinds of remote sensing imagery, in most of the cases in terms of precision, recall, and F1.
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Road map extraction from satellite imagery using connected component analysis and landscape metrics
(2017-07-01)
Jitkajornwanich, Kulsawasd
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Vateekul, Peerapon
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Panboonyuen, Teerapong
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Lawawirojwong, Siam
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Srisonphan, Siwapon
Road map extraction is considered an essential task in GIS as its results are the basis of location-based applications in various domains. Examples include GPS navigation on cell phone, delivery route optimization and planning, tourist attraction locator, and location-based marketing. Satellite imagery, one of the big spatial data sources, is used in this research-though other types of remotely-sensed images can also be applied, such as aerial photographs from aircrafts, UAVs or drones. Despite several methods and techniques proposed and accompanied with different performance criteria, the focus was mainly on the accuracy aspect rather than the completeness of the result sets. That is, the results were said to be satisfactory if it met a certain accuracy criterion associated with some benchmark data sets, regardless of whether all results were retrieved. In many cases; however, both accuracy and completeness are equally important. In this paper, we enhance the result accuracy by incorporating connected component analysis into the method as well as the completeness performance by utilizing an ecology concept, called Landscape Metrics, which describes spatial characteristics, patterns, and correlations of areas/patches through different indices. Two types of metrics are used: shape metrics and isolation metrics. The performance is evaluated based on four criteria: precision, recall, quality, and F1 scores. The results show that more than 90% of performance is achieved in all four criteria.

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