The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image interpretation methods such as colour composite images (false colour composite, true colour composite) were adapted to capture the image for interpreting the visible and shortwave infrared raw bands and thus generating the mapping for the ultramafic terrain. ASTER colour composite image generated from shortwave infrared (SWIR) bands 8 and 4 and NIR band 3 shows contrast signature for the presence of rock types in the ultramafic terrain. The low and high silica percentages to be interpreted through the absorption features of the spectral range from 8.1 to 12 μm. The combinations used to map silica index are of bands 10, 11 and 7 as RGB. PCA was applied to SWIR and visible and near-infrared spectral bands and from PCA output PC 6, 2 and 1 used in the generation of RGB colour composite. Hence, the results of PCA processed image highlights the magnesite mining area by dark red colour. The minimum noise fraction transform (B1, B2, B4) in RGB applied to ASTER image to focus on the overlapped rock types inclusive of magnesite and the same achieved as sharp image output. ASTER band ratios (R3/1, G4/5 and B6/8) in RGB calculated in which the numerator represents shoulders of absorption, and the denominator represents nearest absorption band. Thus, this combination indicates clearly the magnesite mining area in blue colour. The relative band depth in thermal infrared (TIR) bands (B13 and B14) correlated to find the carbonate index is high, medium and low through the pixel ratio output of the ultramafic terrain. Preprocessing of ASTER data involved atmospheric correction using FLAASH algorithm. The minimum noise fraction transform reduces the spectral dimensionality of data in a linear combination of bands. Spectral angle mapper and support vector machines are supervised classification technique adopted that are key learning models with the aid of analyse and categorization of data. The classification defends user, producer, overall accuracy and kappa coefficient. The magnesite mining area and adjust rock samples were field observed, and the mapping of the ultramafic terrain supports the output achieved through chosen ASTER band colour composite, band ratios, relative band depth, silica index, PCA, minimum noise fraction and pixel classification. Thus, a more informative lithological map for the ultramafic terrain generated to discriminate magnesite-mining region.

先进的星载热发射和反射辐射计（ASTER）图像解释方法（例如彩色合成图像（假彩色合成，真彩色合成））适用于捕获图像，以解释可见光和短波红外原始波段，从而生成光谱图。超镁铁质地形。从短波红外（SWIR）波段8和4和NIR波段3生成的ASTER彩色合成图像显示了在超镁铁质地形中存在岩石类型的对比特征。二氧化硅的低百分比和高百分比将通过8.1至12μm光谱范围的吸收特征来解释。用于绘制二氧化硅指数的组合的色带为10、11和7，分别为RGB。PCA被应用于SWIR和可见光和近红外光谱带，并且来自PCA输出PC 6，2和1用于生成RGB彩色复合图像。因此，PCA处理后的图像结果通过深红色突出了菱镁矿开采区域。RGB中的最小噪声分数变换（B1，B2，B4）应用于ASTER图像，以关注包括菱镁矿在内的重叠岩石类型，并获得清晰的图像输出。计算的RGB中的ASTER带比率（R3 / 1，G4 / 5和B6 / 8），其中分子表示吸收的肩部，而分母表示最接近的吸收带。因此，该组合清楚地指示了蓝色的菱镁矿开采区域。通过超镁铁矿地形的像素比输出，相关的热红外（TIR）波段（B13和B14）中的相对波段深度可以发现碳酸盐指数为高，中和低。ASTER数据的预处理涉及使用FLAASH算法进行大气校正。最小噪声分数变换会降低频段的线性组合中数据的频谱维数。频谱角度映射器和支持向量机是监督分类技术，是借助数据分析和分类的关键学习模型。分类捍卫用户，生产者，整体准确性和卡伯系数。现场观察到菱镁矿的开采区域和调整后的岩石样品，超镁铁矿地形的映射支持通过选择的ASTER波段颜色组合，波段比，相对波段深度，二氧化硅指数，PCA，最小噪声分数和像素分类实现的输出。从而，