A new kernel method for hyperspectral image feature extraction

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required

PRELIMINARY DETECTION OF GEOTHERMAL MANIFESTATION POTENTIAL USING MICROWAVE SATELLITE REMOTE SENSING

The satellite technology has developed significantly. The sensors of remote sensing satellites are in the form of optical, Microwave, and LIDAR. These sensors can be used for energy and mineral resources applications. The example of those applications are height model and the potential of geothermal manifestation detection. This study aims to detect the potential of geothermal manifestation using remote sensing. The study area is the Northern of the Inverse Arc of Sulawesi. The method used is remote sensing approach for its preliminary detection with 4 steps as follow (a) mining land identification, (b) geological parameter extraction, (c) preparation of standardized spatial data, and (d) geothermal manifestation. Mining lands identification is using Vegetation Index Differencing method. Geological parameters include structural geology, height model, and gravity model. The integration method is used for height model. The height model integration use ALOS PALSAR data, Icesat/GLAS, SRTM, and X SAR. Structural geology use dip and strike method. Gravity model use physical geodesy approach. Preparation of standardized spatial data with re-classed and analyzed using Geographic Information System between each geological parameter, whereas physical geodesy methods are used for geothermal manifestation detection. Geothermal manifestation using physical geodesy approach in Barthelmes method. Grace and GOCE data are used for gravity model. The geothermal manifestation detected from any parameter is analyzed by using geographic information system method. The result of this study is 10 area of geothermal manifestation potential. The accuracy test of this research is 87.5 % in 1.96 σ. This research can be done efficiently and cost-effectively in the process. The results can be used for various geological and mining applications

Optimized kernel minimum noise fraction transformation for hyperspectral image classification

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the kernel minimum noise fraction (KMNF) transformation, which is a nonlinear dimensionality reduction method. KMNF can map the original data into a higher dimensional feature space and provide a small number of quality features for classification and some other post processing. Noise estimation is an important component in KMNF. It is often estimated based on a strong relationship between adjacent pixels. However, hyperspectral images have limited spatial resolution and usually have a large number of mixed pixels, which make the spatial information less reliable for noise estimation. It is the main reason that KMNF generally shows unstable performance in feature extraction for classification. To overcome this problem, this paper exploits the use of a more accurate noise estimation method to improve KMNF. We propose two new noise estimation methods accurately. Moreover, we also propose a framework to improve noise estimation, where both spectral and spatial de-correlation are exploited. Experimental results, conducted using a variety of hyperspectral images, indicate that the proposed OKMNF is superior to some other related dimensionality reduction methods in most cases. Compared to the conventional KMNF, the proposed OKMNF benefits significant improvements in overall classification accuracy

PENGINDERAAN JAUH UNTUK PENDETEKSIAN AWAL POTENSI TEMBAGA DI SUMBAWA

Tembaga merupakan salah satu jenis mineral penting yang memiliki banyak fungsi dalam berbagai aplikasi. Penelitian ini bertujuan untuk pendeteksian awal tembaga menggunakan data penginderaan jauh. Lokasi penelitian terletak di Sumbawa. Data penginderaan jauh yang digunakan berupa Landsat, ALOS Palsar, X SAR, SRTM C, dan Satelit Geodesi. Landsat digunakan untuk ekstraksi parameter geologi berupa penutup lahan dan perubahannya, bentuk lahan, dan alterasi hidrotermal. ALOS PALSAR, X SAR, dan SRTM C digunakan untuk pembuatan DTM (Digital Terrain Model). Integrasi DTM berguna untuk ekstraksi parameter geologi lainnya berupa struktur dan formasi geologi. DTM yang digunakan memiliki akurasi vertikal + 1,5 m. Data Satelit Geodesi bisa digunakan untuk ekstraksi gaya berat, medan magnet, geodinamika, serta densitas batuan. Berbagai parameter geologi ini diekstraksi dengan metode VIDN, integrasi, dip and strike, interferometri, backscattering, alterasi hidrotermal, geodesi fisis, dan klasifikasi digital berbasis objek. Semua parameter geologi yang telah diekstrak dikorelasikan antar data, sehingga bisa digunakan untuk deteksi potensi tembaga. Informasi geospasial deteksi awal tembaga dan ekstraksi parameter geologinya merupakan produk yang dihasilkan dari penelitian ini. Informasi geospasial ini menggunakan referensi ketelitian ASPRS Accuracy Data for Digital Geospatial Data.Copper is one of the essential mineral that has many functions in variety of applications. This research aimed to detect the copper potential using remote sensing data. The research location is Sumbawa. Remote sensing data used were Landsat, ALOS PALSAR, X SAR, SRTM C, and Satellite Geodesy. Landsat was used for geological parameters extraction such as land cover and its changes, geomorphology, landforms, and hydrothermal alteration. ALOS PALSAR, X SAR and SRTM C were used for height model integration (DTM). This DTM was useful for the other geological parameters extraction, such as geological structures and formations. DTM used has vertical accuracy + 1,5 m. Geodesy Satellite data can be used for the extraction of gravity, magnetic field, geodynamics, and rock densities. These various geological parameters were extracted by VIDN, integration, dip and strike, interferometry, backscattering, hydrothermal alteration, physical geodesy, and classification based digital objects. All of those parameters were then correlated for copper potential detection. The results obtained were geospatial information of copper potential and geological parameters at a scale of 1: 50.000 with reference ASPRS Accuracy Data for Digital Geospatial Data.