Enhanced remote sensing of surface water Chlorophyll-a: Coupling dynamic algae vertical movement modeling with multi-spectral satellite images

Feasibility study on Huanglongbing (citrus greening) detection based on WorldView-2 satellite imagery

Earth observation missions such as Sentinel and Landsat support the large-scale identification of agricultural crops by providing free radar and multispectral satellite images. The extraction of representative image information as well as the combination of different image sources for improved feature selection still represent a major challenge in the field of remote sensing. In this paper, we propose a novel three-dimensional (3D) deep learning U-Net model to fuse multi-level image features from multispectral and synthetic aperture radar (SAR) time series data for seasonal crop-type mapping at a regional scale. For this purpose, we used a dual-stream U-Net with a 3D squeeze-and-excitation fusion module applied at multiple stages in the network to progressively extract and combine multispectral and SAR image features. Additionally, we introduced a distinctive method for generating patch-based multitemporal multispectral composites by selective image sampling within a 14-day window, prioritizing those with minimal cloud cover. The classification results showed that the proposed network provided the best overall accuracy (94.5%) compared to conventional two-dimensional (2D) and three-dimensional U-Net models (2D: 92.6% and 3D: 94.2%). Our network successfully learned multi-modal dependencies between the multispectral and SAR satellite images, leading to improved field mapping of spectrally similar and heterogeneous classes while mitigating the limitations imposed by persistent cloud coverage. Additionally, the feature representations extracted by the proposed network demonstrated their transferability to a new cropping season, providing a reliable mapping of spatio-temporal crop type patterns.

Accurate information on spatial distribution of crop and vegetation indices for crop health monitoring is important for precision agriculture monitoring. However, freely available multispectral satellite images and unmanned aerial vehicle based multispectral images provides great opportunities for crop area estimation and extraction of vegetation indices. An object-based image analysis is better than pixel-based analysis because it is used statistical, geometrical, and topographic feature of the objects. Therefore, this paper presents an object-based image analysis of multispectral satellite and drone images for crop area estimation and extraction of vegetation indices for precision agriculture monitoring. Object segmentation, feature extraction, and classification of multispectral satellite and drone images was done. The experimental results show that the high-resolution drone imagery provides better crop area estimation and vegetation indices compared to freely available coarse resolution satellite imagery due to mixed pixels especially boundary of the crop classes.

Edge enhancement in multispectral satellite images by means of vector operators

Present paper highlights the multispectral high-resolution satellite image has been classified using hybrid convolution neural network. Till now, improving the accuracy of image classification is one of the most significant research area in the domain of remote sensing. The most common deep learning technique such as convolution neural network (CNN) is used for image classification which becomes newer. Extraction of spatial and spectral information from satellite image using the 3D CNN approach is much more complex. While 2D CNN method is mainly used for extraction of spatial information, both spatial and spectral information are available in the multispectral satellite images. In this article, two CNN models (3D and 2D CNN) are integrated into hybrid CNN model which has been applied to multispectral satellite image for extraction of more precise land cover information. The classification results are validated using the overall accuracy and the Kappa statistic which was obtained through compared with the classified data and the Google Earth observation data. Hybrid CNN approach outcome is distinguished with the other methods such as fuzzy C-means (FCM), maximum likelihood classifier (MLC), and self-organizing maps (SOM). The classification accuracy for hybrid CNN model was found 95.17%, which is much higher than the other techniques.KeywordsMultispectral satellite imageClassificationHybrid CNNAccuracy

The urbanization processes and its keen relationship with the spatio-temporal variability of land use-land cover (LULC), land surface temperature and urban heat island (HI) within the district towns in the Rangpur division of Bangladesh has been assessed using multi-spectral Landsat satellite images from 1991 to 2021.The supervised classification approach was used to retrieve LULC types such as water body, built-up area, bare land, agricultural land and vegetation cover. The results of LULC suggest that agricultural and bare land have decreased during the last 30 years in the study areas. The vegetation cover shows an average increase and built-up area has increased progressively from 1991 to 2021. Conspicuously the water bodies, agricultural and bare lands have reduced due to the expansion and rapid growth of settlement areas or urbanization. The spatio-temporal variability of Land Surface Temperature (LST) over the study area responsible for the development of the urban heat islands (UHIs) is assessed using the mono-window algorithm. The areal extents of the UHIs are spreading out day by day and have become most extensive in 2021. Correlation of LULC types with the HI and LST indicated that lower temperature zones were found in the water bodies, vegetated and agricultural lands whereas higher temperature zones were found in the bare lands or highly built-up areas within the district towns. Ground truth data have been validated well with the image processed results with an overall 86.25% classification accuracy which indicates a good level of accuracy of the detected LULC using satellite images. Thus, the research work will help in understanding the land cover dynamics, increasing LST and heat island growth, which could be further used for mitigating the socio-economic hazards faced by the communities in and around the study areas. The Dhaka University Journal of Earth and Environmental Sciences, Centennial Special Volume June 2022: 17-28

Multispectral image analysis for monitoring by IoT based wireless communication using secure locations protocol and classification by deep learning techniques

As a key parameter of land surface energy balance models, near surface air temperature (NSAT) is an important indicator of the surface atmospheric environment and the urban thermal environment. At present, NSAT data are mainly captured by meteorological ground stations. In areas with a sparse distribution of meteorological stations, however, it is not possible to describe the heterogeneity of NSAT in continuous space. With the rapid development of satellite remote sensing technologies, there is now a significant method to retrieve NSAT from multispectral satellite images based on machine learning methods. In the literatures published so far, there is little reported research concerning the comprehensive evaluation and/or the systematic comparison of NSAT retrieval performances based on different machine learning models. Hence, the three most commonly-used machine learning models, Support Vector Regression (SVR), Multilayer Perceptron Neural Network (MLBPN), and Random Forest (RF), have been employed for the NSAT retrieval from various multispectral satellite images of MODIS daytime and nighttime data, Landsat 8 data, and Sentinel-2 data. Comparison of the NSAT retrieval results generated by the different machine learning models from the different types of satellite images reveals that (a) the RF-based model has a better NSAT retrieval performance than the SVR- or MLBPN-based models with respect to both the accuracy and stability, and (b) the NSAT results retrieved from the MODIS data were generally better than those from the Landsat 8 and Sentinel-2 data. To sum up, the conducted research in this article does not only provide a reference for practical applications relevant to NSAT retrievals, but also proposes an efficient RF-based model for NSAT retrieval from multispectral satellite images in continuous space.

A mosaic of two WorldView-2 high resolution multispectral images (Acquisition dates: October 2010 and April 2012), in conjunction with field survey data, was used to create a habitat map of the Danajon Bank, Philippines (10°15'0'' N, 124°08'0'' E) using an object-based approach. To create the habitat map, we conducted benthic cover (seafloor) field surveys using two methods. Firstly, we undertook georeferenced point intercept transects (English et al., 1997). For ten sites we recorded habitat cover types at 1 m intervals on 10 m long transects (n= 2,070 points). Second, we conducted geo-referenced spot check surveys, by placing a viewing bucket in the water to estimate the percent cover benthic cover types (n = 2,357 points). Survey locations were chosen to cover a diverse and representative subset of habitats found in the Danajon Bank. The combination of methods was a compromise between the higher accuracy of point intercept transects and the larger sample area achievable through spot check surveys (Roelfsema and Phinn, 2008, doi:10.1117/12.804806). Object-based image analysis, using the field data as calibration data, was used to classify the image mosaic at each of the reef, geomorphic and benthic community levels. The benthic community level segregated the image into a total of 17 pure and mixed benthic classes.

Maps are vital tools for most government agencies and consumers. However, their manual generation and updating is tedious and time consuming. As a step toward automatic map generation, we introduce a novel system to detect houses and street networks in IKONOS multispectral images. Our system consists of four main blocks: multispectral analysis to detect cultural activity, segmentation of possible human activity regions, decomposition of segmented images, and graph theoretical algorithms to extract the street network and to detect houses over the decompositions. We tested our system on a large and diverse data set. Our results indicate the usefulness of our system in detecting houses and street networks, hence generating automated maps.

A Quickbird, Ikonos and Landsat TM multi-spectral image data of Kadavu, Fiji was used in a multi-scale segmentation and object-based image classification to produce this map consisting of reef type, geomorphic zones and benthic community type. This classified map is provided in ArcMap shapefile format. Projection used was Universal Transverse Mercator Zone 60 South and Datum used was WGS 84.

This work proposes an efficient classification scheme for identifying various land classes present in a multispectral satellite image. This spectral image provides extensive knowledge about land cover mapping in multispectral satellite images. This paper proposes an efficient technique in land cover classification which involves fuzzy hybrid with hierarchical clustering applied then to the sparse SVM classifier. Initially preprocessing is done using Gaussian filter and transformed to a suitable form using Wavelet transform. Subsequently, segmentation is performed in the wavelet transformed image using fuzzy hybrid with hierarchical clustering technique. Then the proposed sparse SVM classifier is trained by the features obtained from the clustered output. Thus the multispectral image of various satellite images can be classified into different land classes comparing with the training data given to sparse SVM. The performance is evaluated by comparing with the existing classifiers for different multi-spectral satellite images which provides accurate results. The classification accuracy is measured from the performance analysis graph where the results demonstrate that the proposed sparse SVM classifier can optimally enhance the classification accuracy of any multispectral satellite image.

Automated identification of land cover type using multispectral satellite images

Abstract: Dehazing multispectral satellite images is a crucial remote sensing activity since it raises the calibre and precision of satellite images. This research paper presents a comparative analysis of two approaches, namely histogram equalization and an algorithm that combines boundary constraint and contextual regularization methods for efficient dehazing of multispectral images. The algorithms successfully eliminates haze from multispectral satellite images, while preserving their features and structural integrity. Experimental results demonstrate that the latter approach outperforms the other dehazing algorithm in terms of both visual quality and quantitative measurements.

Remote sensing techniques are a powerful tool for monitoring littoral zones. Optical sensors can be used to quantify water quality parameters such as suspended sediments. It is possible to estimate the Total Suspended Matter (TSM) concentration using multi-spectral satellite images. In order to extract meaningful information, the satellite data needs to be validated with in situ measurements. The main objective of this work was to quantify the TSM in sea breaking zone, using multi-spectral satellite images. A part of the northwest coast of Portugal, centered around Aveiro, was chosen as a test area. Several methodologies have been used to establish a relationship between the above sea water reflectance and the TSM concentration. Various field trips were done in order to simultaneously obtain water samples and reflectance measurements. A relationship between TSM concentration and reflectance was established for the range 400 - 900 nm. Data from Landsat TM, SPOT HRVIR and ASTER were calibrated and geometric corrected. The reflectance values were used to estimate the TSM concentration using the relationships established using the field measurements. The model coefficients and correlation factors, for identical bands on different sensors, presented a high similarity. The results have been incorporated in a Geographical Information System (GIS).