Utilizzo delle texture nella classificazione di vegetazione in immagini ad altissima risoluzione acquisite da UAS

Nell'ambito di un progetto di ricerca di Dottorato in Geomatica, relativo alla messa a punto di un sistema di rilievo multisensore a pilotaggio remoto, sono state analizzate immagini multispettrali e multitemporali acquisite tramite rilievi da UAS. Come area test, è stata scelta una porzione di un vivaio di piante: l'altissima risoluzione delle immagini rende visibili dettagli che possono consentire il riconoscimento delle diverse specie vegetali. Per automatizzare il processo, accanto alle più tradizionali classificazioni pixel-based (basate cioè sulle informazioni radiometriche multispettrali direttamente contenute nelle immagini), si può far ricorso all'utilizzo di variabili derivate di tipo geometrico, che tengano conto delle relazioni spaziali delle variazioni radiometriche. L'uso delle variabili di texture a queste risoluzioni, ben differenti da quelle di immagini satellitari o aeree, è ancora da esplorare. In questo lavoro sono perciò presentati alcuni esperimenti di ottimizzazione di procedure di classificazione, mediante l'uso combinato di variabili radiometriche tradizionali (come RGB e NDVI) e di variabili di texture opportunamente selezionate. In particolare, tali variabili sono state generate con finestre di dimensione crescente; la dimensione ottimale è stata poi individuata tramite analisi dei semivariogrammi. L'aggiunta delle variabili di texture a bande e indici tradizionali ha portato ad un incremento del 17% dell'accuratezza totale di classificazioni con algoritmo supervisionato (Maximum Likelihood)

Object-based Analysis and Multispectral Low-altitude Remote Sensing for Low-cost Mapping of Chalk Stream Macrophytes

Their small size and high biodiversity have until now made UK chalk streams unsuitable subjects for study with remote sensing techniques. Future technological developments are however likely to change this. The study described in this paper shows how high resolution multispectral images taken with an off-the-shelve, infrared sensitive digital camera, can give a first insight into future opportunities for mapping and monitoring of submerged chalk stream environments. The high resolution multispectral images have been used in combination with Object Based Image Analysis (OBIA) techniques to map submerged vegetation. Preliminary results show that the Near Infrared Red information recorded by the camera greatly improves the classification of individual macrophyte species. The benefit of the object-based image analysis approach is at the presented stage only limited, but a first attempt at creating a robust rule set has been applied to photos taken at two different field sites with some success. The analysis also showed how texture features are useful for the separability between macrophyte classes. Overall the results are promising for further applications of remote sensing techniques to chalk streams as well as for application of the low cost sensor set-up

A GRAPHIC PROCESSING UNIT FRAME WORK FOR CONVOLUTIONAL NEURAL NETWORK BASED CLASSIFICATION OF REMOTELY SENSED SATELLITE IMAGES

Near real time processing and feature extraction from high-resolution satellite images aids in various applications of remote sensing including segmentation, classification and change detection. The latest generation of satellite sensors are able to capture the data at a very high spatial, spectral and temporal resolution. The processing time required for such a huge data is also large. Disaster monitoring applications such as forest fire monitoring, earthquakes require fast/real time processing of high resolution data to enable response activities. In general, due to the large size of satellite data, the computational time of feature calculation and training neural network is found to be very high. Therefore in order to achieve the aim of near real time processing of such huge data, we developed a parallel implementation. The implementation is performed on NVIDIA’s Graphical Processing Unit. The performance improvement obtained is demonstrated by a GPU implementation on Resourcesat-1 data and compared with the traditional sequential implementation. The results show that the GPU implementation is found to achieve performance improvement in terms of execution time and speedup throughput as compared to the sequential implementation

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

A family of 26 non-parametric texture descriptors based on Histograms of Equivalent Patterns (HEP) has been tested, many of them for the first time in remote sensing applications, to improve urban classification through object-based image analysis of GeoEye-1 imagery. These HEP descriptors have been compared to the widely known texture measures derived from the gray-level co-occurrence matrix (GLCM). All the five finally selected HEP descriptors (Local Binary Patterns, Improved Local Binary Patterns, Binary Gradient Contours and two different combinations of Completed Local Binary Patterns) performed faster in terms of execution time and yielded significantly better accuracy figures than GLCM features. Moreover, the HEP texture descriptors provided additional information to the basic spectral features from the GeoEye-1's bands (R, G, B, NIR, PAN) significantly improving overall accuracy values by around 3%. Conversely, and in statistic terms, strategies involving GLCM texture derivatives did not improve the classification accuracy achieved from only the spectral information. Lastly, both approaches (HEP and GLCM) showed similar behavior with regard to the training set size applied

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen’s kappa coefficient of 0.84 and an overall accuracy of 85%

Combining Unmanned Aerial Vehicle (UAV)-Based Multispectral Imagery and Ground-Based Hyperspectral Data for Plant Nitrogen Concentration Estimation in Rice

Plant nitrogen concentration (PNC) is a critical indicator of N status for crops, and can be used for N nutrition diagnosis and management. This work aims to explore the potential of multispectral imagery from unmanned aerial vehicle (UAV) for PNC estimation and improve the estimation accuracy with hyperspectral data collected in the field with a hyperspectral radiometer. In this study we combined selected vegetation indices (VIs) and texture information to estimate PNC in rice. The VIs were calculated from ground and aerial platforms and the texture information was obtained from UAV-based multispectral imagery. Two consecutive years (2015 & 2016) of experiments were conducted, involving different N rates, planting densities and rice cultivars. Both UAV flights and ground spectral measurements were taken along with destructive samplings at critical growth stages of rice (Oryza sativa L.). After UAV imagery preprocessing, both VIs and texture measurements were calculated. Then the optimal normalized difference texture index (NDTI) from UAV imagery was determined for separated stage groups and the entire season. Results demonstrated that aerial VIs performed well only for pre-heading stages (R2 = 0.52–0.70), and photochemical reflectance index and blue N index from ground (PRIg and BNIg) performed consistently well across all growth stages (R2 = 0.48–0.65 and 0.39–0.68). Most texture measurements were weakly related to PNC, but the optimal NDTIs could explain 61 and 51% variability of PNC for separated stage groups and entire season, respectively. Moreover, stepwise multiple linear regression (SMLR) models combining aerial VIs and NDTIs did not significantly improve the accuracy of PNC estimation, while models composed of BNIg and optimal NDTIs exhibited significant improvement for PNC estimation across all growth stages. Therefore, the integration of ground-based narrow band spectral indices with UAV-based textural information might be a promising technique in crop growth monitoring

BVLOS UAV missions for vegetation mapping in maritime Antarctic

Polar areas are among the regions where climate change occurs faster than on most of the other areas on Earth. To study the effects of climate change on vegetation, there is a need for knowledge on its current status and properties. Both classic field observation methods and remote sensing methods based on manned aircraft or satellite image analysis have limitations. These include high logistic operation costs, limited research areas, high safety risks, direct human impact, and insufficient resolution of satellite images. Fixed-wing unmanned aerial vehicle beyond the visual line of sight (UAV BVLOS) missions can bridge the scale gap between field-based observations and full-scale airborne or satellite surveys. In this study the two operations of the UAV BVLOS, at an altitude of 350m ASL, have been successfully performed in Antarctic conditions. Maps of the vegetation of the western shore of Admiralty Bay (King George Island, South Shetlands, Western Antarctic) that included the Antarctic Specially Protected Area No. 128 (ASPA 128) were designed. The vegetation in the 7.5 km2 area was mapped in ultra-high-resolution(<5cm and DEM of 0.25m GSD), and from the Normalized Difference Vegetation Index (NDVI), four broad vegetation units were extracted: “dense moss carpets” (covering 0.14 km2 ,0.8%ofASPA128), “Sanionia uncinata moss bed” (0.31 km2 , 1.7% of ASPA 128), “Deschampsia antarctica grass meadow” (0.24 km2,1.3% of ASPA 128), and “Deschampsia antarctica–Usnea antarctica heath” (1.66 km2,9.4% of ASPA 128). Our results demonstrate that the presented UAV BVLOS–based surveys are time-effective (single flight lasting 2.5 h on a distance of 300 km) and cost-effective when compared to classical field-based observations and are less invasive for the ecosystem. Moreover, unmanned airborne vehicles significantly improve security, which is of particular interest in polar region research. Therefore, their development is highly recommended for monitoring areas in remote and fragile environments. KEYWORD

Predicting Tropical Dry Forest Successional Attributes from Space: Is the Key Hidden in Image Texture?

Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (R-2 = 0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information

Improving tree species classification using UAS multispectral images and texture measures

This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User's and Producer's Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys