3D Reconstruction Approach for Outdoor Scene Based on Multiple Point Cloud Fusion Abstract Multiple point cloud fusion is one of the most widely used methods for outdoor scene 3D reconstruction. However, being based on the traditional registration methods, their performance critically influences the quality of the 3D reconstruction. This paper proposes a 3D reconstruction method that fuses different sensors point cloud, which comes from laser scanning and structure from motion. First, a scale-based principal component analysis–iterative closest point (a scaled PCA–ICP) algorithm is addressed to eliminate different scales of two view points. Further, the feature points are extracted automatically for accurate registration by analyzing the persistence of feature points with discretely sampling on different sphere radii. Finally, the optimization ICP method is used to match multiple point cloud to achieve accurate reconstruction of outdoor scenes robustly. The experimental evaluation demonstrates that the proposed method is able to produce reliable registration results for the outdoor scene.

Study of the Behavior of Super Resolution on Soft-Classified Output Abstract Once the satellite sensor is in orbit, no hardware enhancement of the lens assembly can be done to improve spatial and spectral resolution. Super resolution (SR) as single frame or multi-frame can solve this problem up to a large extent. In this study, single- and multi-frame SR techniques were applied and tested on Worldview-2 datasets as well as on across-spatial datasets of LISS III and LISS IV. Study of soft classifier’s behavior on super-resolved images was performed through possibilistic C-means classifier. Quantitative methods based on calculation of peak signal-to-noise ratio, mean square error, root means square error, image quality index and qualitative methods of visual interpretation proved that both super-resolution methods remove outliers in an efficient way and resulted in images containing sharp edges. The single-frame super-resolution technique was found relatively inferior in terms of contrast and spatial resolution. Overall, multi-frame SR method outperformed other methods.

Water Requirement for Irrigation of Complicated Agricultural Land by Using Classified Airborne Digital Sensor Images Abstract Land use of irrigated areas nearby the metropolitan is complex. On fields, crop growth may differ with variations in the water demand. Among the image classification methods, combined object-oriented classification is currently preferred over conventional pixel-based classification. Compared with the traditional pixel-based method, which generally exhibits a spot-like salt-and-pepper effect, object-based classification can significantly reduce the salt-and-pepper effect and amount of data required for analysis. To obtain improved spectral recognition, maximum image information is described using color, texture, and shape to enhance image recognition. In this study, image information extraction and crop interpretation were performed using the airborne digital sensor ADS40 to obtain experimental data, and the traditional supervised image and image object classification methods were compared. The results indicate that both the image classification methods could yield an overall accuracy of more than 80%, and the accuracy of object-based classification (88.68%) was higher than that of the other classification. The daily water requirement of crops in the study area, calculated using a high-precision image object classification method, was approximately 2585 m3. The current results may aid in the effective estimation of agricultural irrigation water consumption.

Mapping Tea Plantations from Multi-seasonal Landsat-8 OLI Imageries Using a Random Forest Classifier Abstract It is very challenging to extract tea plantations from medium-resolution satellite imageries. This paper presents a new methodological framework based on the random forest classifier for extracting tea plantations from Landsat-8 OLI imageries. Analysis is facilitated by a dataset of three Landsat-8 OLI images (spring, autumn and winter) in 2014 covering the Anji County, one of the major tea production regions in China. More specifically, in order to determine the relative importance of spectra, texture, vegetation index and seasonality on classification accuracy, we design a series of classification feature sets, including: initial feature sets for different seasons, feature selection feature sets for different seasons and multi-seasonal feature sets. The results show that the multi-seasonal feature selection set has the best feature set performance (PA = 0.88; OA = 0.92; Kappa = 0.90). Our study demonstrates that the random forest classifier is reliable and practical for extracting tea plantations from medium-resolution images. Highlights of this study are: mapping tea plantations, an important cash crop to local agriculture, in a fragmented landscape; integrating textual, vegetational, and seasonal features, which were helpful for improving tea plantation mapping accuracy in combination; taking advantage of the feature selection function of random forest, supporting high-dimensional data classification, which leads to a higher classification accuracy.

Mapping Glacial Geomorphology and Livelihood Resources in Urgos Watershed, Lahul and Spiti District, Himachal Pradesh, India Abstract The Urgos watershed is situated in the rain shadow zone of the Pir Panjal Range of the Lahul Himalaya, where western disturbances dominate with solid precipitation. Consequently ice, permafrost and snow meltwater is the main source of the Urgos Nala (stream), which supports agriculture and replenishes drinking water sources downstream in the watersheds. The effect of small amount of glacier retreat and changes in seasonal snow cover is critical for the functioning of meltwater and high natural resources dependent mountain communities. Agriculture, vegetation, fodder and pasture land in the watershed are all entirely dependent on meltwater. Therefore, the study aims to make a quantitative and large-scale map of the study area in relation to rural livelihood. Resource mapping (1:5000) and quantitative characterization of Urgos watershed are achieved using high-resolution satellite images, digital elevation models, Total Station mapping, differential Global Positioning System and collection of field evidences. The landform evolution in the watershed is a result of intense glaciofluvial processes in the past as well as present. The geomorphic features mapped in the area reveal multiple glacial advances in the watershed, in the past. This has direct links with climatic fluctuations and its impact on agriculture and allied activities for the sustenance of people. The analysis shows 22.49% area under glacier and only 1.04% area of the entire watershed under agriculture, fodder, pasture and vegetation land. This 1.04% area of the watershed plays a significant role in the livelihood of the people.

Validation of GaoFen-1 Satellite Geometric Products Based on Reference Data Abstract Applications of high-resolution remote sensing satellite data are becoming increasingly extensive. China has launched a series of high-resolution optical satellites: GaoFen-1 (GF-1), GaoFen-2, etc., and published several remote sensing image products. However, validation of the long time series of these satellite geometric products is lacking. Therefore, this study selects 2013–2016 GaoFen-1 satellite geometry products for the Beijing area over four years, obtaining a total of 164 images, and uses ZY-3 satellite products to verify the accuracy of the data set. Moreover, the positioning accuracy of the geometric products of two camera types carried by GF-1 (wide field of view, WFV; panchromatic and multispectral, PMS) is analyzed. The results yield the following conclusions. (1) GF-1 satellite geometric products have obvious systematic errors, and the error value varies significantly with the orbit time. (2) The internal distortion of the GF-1 satellite image is well controlled, and the geometric offset magnitude and direction of ground control points (GCPs) are very similar in the interior of each scene. (3) In a short period of time (20 days), the offset direction of the four WFV cameras is very similar, but there is a small difference in the mean plane offset (RMSE2D). The RMSE2D and offset direction of the two PMS cameras are very similar. (4) In the long term, the relative offset of the WFV and PMS cameras varies with the satellite orbit time. At the first year of the satellite orbit time (in 2014), the relative offsets of four WFV cameras increased to maximum (168.96 m, 171.12 m, 226.65 m, and 207.04 m). From 2015 to 2016, the offsets decreased to a relatively stable state (within 100 m). (5) This validation method based on reference data is shown to be feasible. This study provides an important reference for the application of high-resolution remote sensing satellite products.

Fuzzy Multi-level Color Satellite Image Segmentation Using Nature-Inspired Optimizers: A Comparative Study Abstract In the realm of image processing domain, segmentation is an indispensable method for various applications. One can segment an image according to shape, size, regularities, structure, color, etc. Multi-level thresholding for image segmentation is one of the most promising methods for segmentation in the recent era. However, multi-level thresholding is computationally expensive, tedious and also challenging because of finding the optimal threshold values. Thus, to address this issue, this study presents a stochastic fractal search (SFS) with fuzzy entropy-based multi-level thresholding model for the proper segmentation of color satellite images. To prove the superiority of SFS algorithm, a comparative study is performed with four well-known nature-inspired optimization algorithms, namely particle swarm optimization (PSO), cuckoo search (CS), harmony search (HS) and artificial bee colony (ABC) algorithms. The experiment has been conducted on various satellite images, and the result shows that SFS with fuzzy entropy-based model provides superior-quality segmented images over other methods in terms of fitness value, computational time and values of quality metrics. The experimental study also shows that computational time of SFS algorithms is 2.5% less than CS algorithms and 8%, 9%, 15% less than ABC, PSO, HS, respectively, on average when the same number function evaluations has been considered as stopping criterion.

Use of Logistic Regression in Land-Cover Classification with Moderate-Resolution Multispectral Data Abstract The current study highlights the use of binary logistic regression for land-use land-cover (LULC) classification. The moderate-resolution Sentinel-2 multispectral data was used for LULC map generation for the post-monsoon season. The main focus of this study is to present a simple and precise approach for image classification using binary logistic regression (BLR) technique. The study was carried out in cropland, fallow land, forest and water body dominated subtropical region of India located in the eastern coastal region. The cropland and fallow lands are mostly dependent on the monsoon and reciprocal land covers. A large number of training and testing data points were collected viewing the image in a standard false-color composite. ArcGIS, Microsoft Office Excel and R software were used for classification. In addition to BLR, the training and testing data points were also used to perform the classification with ‘random forest’ classifier in R. We observed higher classification accuracy for spectrally pure classes and pixels and lower for closely associated mix-pixels. Lower user’s and producer’s accuracies (< 90%) were observed for fallow land, water body and grassland class during training and model building and for fallow land and forest during accuracy assessment, whereas the accuracies were more than 90% for the rest of classes during both training and testing. Misclassifications were mostly observed between forest, fallow land, grassland and water body during training, which were forest and fallow land in testing, due to their lower spectral difference with reference to classified classes. However, the overall accuracy and kappa value during training and testing were more than 94% and 0.98, respectively. Similar accuracies and misclassification were also obtained with the results of random forest model, validating the adopted methodology. Regardless of the seasonal variations in cropland and fallow land, the field observations (52 locations) also corroborated the estimated classification accuracy. The easy implementation and comparatively higher classification accuracy with the binary logistic technique are believed to increase its intense use in land-cover classification.

Lithology and Structural Mapping of Kadavur Basin, Tamil Nadu, India, Using IRS P6 LISS III Satellite Data Abstract The Kadavur basin is located in the Southern Granulite Terrain, central part of Tamil Nadu, India. The basin structure and presence of anorthositic formation in the Kadavur are interesting features for many researchers. Anorthosite is an important igneous rock, with limited coverage on earth crust, and commonly present on lunar surface. The origin and emplacement of anorthosite in terrestrial environment are always a matter of interest among geoscientist. In the present study, moderate-resolution IRS P6 LISS III multispectral satellite data are used to interpret the lithology, structure and morphology of the Kadavur basin. The processed satellite outputs show contrast signatures for various rock types such as anorthosites, gabbro, quartzite, gneiss, augen gneiss and charnockites. The lithological and structural details collected during the field investigation were used for mapping. The DEM-generated image shows detail information on structural features including crust line, dipping beds and fault zone in the Kadavur basin. The circular quartzite ridges formed as major structural hill systems, dipping vertically inward toward the center of the basin. The lineation indicates the axis of the fold structure standing on edge or plunging very steeply. The various theories proposed for the origin of structural basin and emplacement of gabbroic anorthositic plutonic body in the Kadavur basin are discussed.

Assessing the Human Role in Changing Floodplain and Channel Belt of the Yamuna River in National Capital Territory of Delhi, India Abstract Ongoing demands for irrigation, drinking water supplies and hydropower generation encourage human beings to construct dams and reservoirs on rivers. Such engineering structures together with embankments may protect downstream areas from flooding, can give short-term benefits but may degrade the natural or pristine condition of a river by altering the longitudinal and lateral connectivity of water and sediments. This study analyses the human-induced changes in the channel belt and floodplain morphology of the Yamuna River between Wazirabad and new Okhla barrages using topographic maps and satellite image. In the pre-dam condition (1867–1868), the Yamuna River had a highly braided channel pattern compared to the post-dam situation. After construction of the Tajewala, Hathnikund and Wazirabad barrages, the longitudinal connectivity of sediments and discharge were disturbed so that bar areas were reduced and values of the braided index declined. Dam-induced moderation of peak discharges, rapidly growing population, increasing length of embankments and roads in the floodplain are the major reasons behind the urbanization of Yamuna floodplain in Delhi. This study also shows that the increasing length of engineering structure caused a reduction in channel belt area and its width. Results of the present study are useful for the environmentalists, policy makers and earth scientists working on the restoration and management of floodplain between Wazirabad and new Okhla barrage.