Graph Laplacian for Image Anomaly Detection

Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the graph Fourier transform, we are able to overcome some of RXD's limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems

EVALUASI PERUBAHAN LUASAN TERUMBU KARANG PADA KAWASAN PULAU MENJANGAN (STUDI KASUS: PULAU MENJANGAN, BALI)

Pulau Menjangan merupakan sebuah pulau kecil yang terletak pada Barat Laut pulau Bali dan masuk dalam kawasan Taman Nasional Bali Barat (TNBB) yang sangat berpotensi untuk menjadi tempat lokasi wisata air berupa diving maupun snorkeling dikarenakan keanekaragaman flora dan fauna laut di pulau tersebut. Telah terjadi penurunan luasan terumbu karang sebanyak 2,02 hektar pada tahun 2007-2009. Pemetaan sebaran terumbu karang dilakukan dengan menggunakan metode penginderaan jauh. Citra satelit yang digunakan aalah Landsat 8 dengan menggunakan algoritma Lyzenga. Kanal yang digunakan yaitu kanal biru dan hijau dikarenakan kedua kanal tersebut memiliki nilai spektral tertinggi. Koreksi penghilangan efek kedalaman air digunakan untuk pemrosesan data. Klasifikasi tak terbimbing dilakukan untuk penentuan obyek dalam citra yang sepenuhnya diberikan kuasa pada perangkat lunak. Hasil klasifikasi menunjukkan adanya penurunan luasan terumbu karang sebesar 1,8 hektar pada tahun 2013-2015. ========== Menjangan island is a small island that located in Northwest of Bali province, which is inside the area of Taman Nasional Bali Barat (TNBB) and its very potential to become a spot of marine tourism for diving and snorkeling because it has many types of flora and fauna in that island. There has been a drop in the extent of coral reefs as much as 2.02 hectare in 2007-2009. Mapping of coral reefs distribution is done by using remote sensing method. Using Landsat 8 satellite’s images with Lyzenga algorithm. Landsat 8 Satellite imagery was used with the processing performed on the blue bands and green bands because both of those bands have the highest spectral value. Correction of the effect of water depth elimination is used for data processing. Unsupervised classification is done for object determination in satellite’s images is fully process by software. Classification result showed that there was decrease of coral reefs as 1.8 hectare in 2013-2015