A Multi Stage Approach for Urban Building Extraction from Remote Sensing Satellite Images

The most important parameter for urban information system is the building information which is represented by the geographic location of the buildings as well as the area, perimeter, density, inter building distances. This data is integrated with demographic data for various applications. High resolution Remote sensing images are widely used as primary data for automatic extraction of building information. Many researchers have developed different methods for maximizing the detection percentage with minimum errors. This paper analyzes the primary data available for researchers, deriving the secondary information and utilizing it effectively. Case studies by various researchers were analyzed and a methodology has been outlined using their experiences, which is expected to be more efficient and reduced errors.

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HIGH-RESOLUTION REMOTE SENSING IMAGE BUILDING EXTRACTION BASED ON MARKOV MODEL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-2411-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 2411-2418 ◽

Cited By ~ 1

Author(s):

W. Zhao ◽

L. Yan ◽

Y. Chang ◽

L. Gong

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Markov Model ◽

Remote Sensing Image ◽

Image Features ◽

Building Extraction ◽

Texture Information ◽

Building Information ◽

Building Area ◽

Image Building

With the increase of resolution, remote sensing images have the characteristics of increased information load, increased noise, more complex feature geometry and texture information, which makes the extraction of building information more difficult. To solve this problem, this paper designs a high resolution remote sensing image building extraction method based on Markov model. This method introduces Contourlet domain map clustering and Markov model, captures and enhances the contour and texture information of high-resolution remote sensing image features in multiple directions, and further designs the spectral feature index that can characterize “pseudo-buildings” in the building area. Through the multi-scale segmentation and extraction of image features, the fine extraction from the building area to the building is realized. Experiments show that this method can restrain the noise of high-resolution remote sensing images, reduce the interference of non-target ground texture information, and remove the shadow, vegetation and other pseudo-building information, compared with the traditional pixel-level image information extraction, better performance in building extraction precision, accuracy and completeness.

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Building Extraction and Number Statistics in WUI Areas Based on UNet Structure and Ensemble Learning

Remote Sensing ◽

10.3390/rs13061172 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1172

Author(s):

De-Yue Chen ◽

Ling Peng ◽

Wei-Chao Li ◽

Yin-Da Wang

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Ensemble Learning ◽

Semantic Segmentation ◽

Regional Governance ◽

Building Extraction ◽

Contour Extraction ◽

Remote Sensing Images ◽

Building Information

Following the advancement and progression of urbanization, management problems of the wildland–urban interface (WUI) have become increasingly serious. WUI regional governance issues involve many factors including climate, humanities, etc., and have attracted attention and research from all walks of life. Building research plays a vital part in the WUI area. Building location is closely related with the planning and management of the WUI area, and the number of buildings is related to the rescue arrangement. There are two major methods to obtain this building information: one is to obtain them from relevant agencies, which is slow and lacks timeliness, while the other approach is to extract them from high-resolution remote sensing images, which is relatively inexpensive and offers improved timeliness. Inspired by the recent successful application of deep learning, in this paper, we propose a method for extracting building information from high-resolution remote sensing images based on deep learning, which is combined with ensemble learning to extract the building location. Further, we use the idea of image anomaly detection to estimate the number of buildings. After verification on two datasets, we obtain superior semantic segmentation results and achieve better building contour extraction and number estimation.

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A Stacking Ensemble Deep Learning Model for Building Extraction from Remote Sensing Images

Remote Sensing ◽

10.3390/rs13193898 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3898

Author(s):

Duanguang Cao ◽

Hanfa Xing ◽

Man Sing Wong ◽

Mei-Po Kwan ◽

Huaqiao Xing ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Optimization Method ◽

Learning Model ◽

Ensemble Model ◽

Building Extraction ◽

Remote Sensing Images ◽

Basic Model ◽

Building Information ◽

Deep Learning Model

Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy.

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An Efficient Multi-stage Object-Based Classification to Extract Urban Building Footprints from HR Satellite Images

Traitement du signal ◽

10.18280/ts.380120 ◽

2021 ◽

Vol 38 (1) ◽

pp. 191-196

Author(s):

Gopala Krishna VSSN Pendyala ◽

Hemantha Kumar Kalluri ◽

Venkateswara C. Rao

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Satellite Images ◽

Airborne Lidar ◽

Surface Model ◽

Digital Surface Model ◽

Multi Stage ◽

Object Based ◽

Normalised Difference Vegetation Index ◽

Remote Sensing Satellite

Urban building information can be effectively extracted by applying object-based image segmentation and multi-stage thresholding on High Resolution (HR) remote sensing satellite imageries. This study provides the results obtained using this method on the images of Indian remote sensing satellite, CARTOSAT-2S launched by the Indian Space Research Organization (ISRO). In this study, a method is developed to extract urban building footprints from the HR remote sensing satellite images. The first step of the process consists of generating highly dense per pixel Digital Surface Model (DSM) by using semi global matching algorithm on HR satellite stereo images and applying robust ground filtering to generate Digital Terrain Model (DTM). In the second step, multi-stage object-based approach is adopted to extract building bases using the PAN sharpened image, normalized Digital Surface Model (nDSM) derived from DSM and DTM, and Normalised Difference Vegetation Index (NDVI). The results are compared with the manual method of drawing building footprints by cartographers. An average precision of 0.930, recall of 0.917, and f-score of 0.922 are obtained. The results are found to be in a match with the method using the high resolution Airborne LiDAR DSM by providing a solution for large areas, low cost and low time.

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The machine training in problems of satellite images’s processing

Metrologiya ◽

10.32446/0132-4713.2020-4-15-37 ◽

2020 ◽

pp. 15-37

Author(s):

L. P. Bass ◽

Yu. A. Plastinin ◽

I. Yu. Skryabysheva

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Computer Vision ◽

Satellite Images ◽

Vision Systems ◽

Earth Remote Sensing ◽

Practical Applications ◽

Convolution Neural Networks ◽

Computer Vision Systems ◽

Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

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Application of Remote Sensing in Agriculture

Geoinformatica Polonica ◽

10.2478/gein-2014-0007 ◽

2014 ◽

Vol 13 (1) ◽

Cited By ~ 3

Author(s):

Jan Piekarczyk

Keyword(s):

Remote Sensing ◽

Precision Agriculture ◽

Crop Production ◽

Satellite Images ◽

Crop Yields ◽

Spatial Scales ◽

Strong Relationship ◽

Physical Parameters ◽

Agricultural Crop ◽

Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

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TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

Revista da Casa da Geografia de Sobral (RCGS) ◽

10.35701/rcgs.v21n2.548 ◽

2019 ◽

Vol 21 (2) ◽

pp. 1310-1320

Author(s):

Cícera Celiane Januário da Silva ◽

Vinicius Ferreira Luna ◽

Joyce Ferreira Gomes ◽

Juliana Maria Oliveira Silva

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Temperature Measurement ◽

Rainy Season ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Low Density ◽

Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia. ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

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Role of Finance in Resource use Efficiency and Constraints in Banana Cultivation in Uttar Pradesh

Indian Journal of Economics and Development ◽

10.35716/ijed/ns20-016 ◽

2020 ◽

pp. 437-441

Keyword(s):

Resource Use ◽

Uttar Pradesh ◽

Sampling Technique ◽

Farm Size ◽

Primary Data ◽

Resource Use Efficiency ◽

Regular Source ◽

Block Level ◽

Use Efficiency ◽

Secondary Information

The study was conducted using purposive cum random sampling technique and two hundred respondents comprised of 100 each borrowers and non-borrowers were selected from two block of district including marginal, small and medium categories of farm size. Primary data were collected through personal interview technique and required secondary information was taken from the record available at district and block level. Simple tabular and functional analysis and Garrett ranking were done to draw inferences. As per the result obtained from the study, no much difference was seen between the resource use efficiency of borrower and non-borrower farms and constraints faced by borrower. Since banana is a cash crop and it needs initial costs for its establishment, and after harvesting the crop regular source of income was generated by selling of suckers (seed) plant and its fruits. It’s by-product, leaves, etc. also used for various purposes. Minute inspection of the analysis showed that finance played important role for initiating the cultivation of banana crops showed the resource use efficiency that there is no considerable difference found on sample farms of borrower and non-borrower categories. Constraints faced by majority of the farmers were mainly delay in disbursement of loan and lack of the repayment period insufficient and improper management for withdraws on KCC.

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